model diffusion experiment

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Tea bag diffusion!

January 2, 2012 By Emma Vanstone 9 Comments

I love a good cup of tea. In fact, I cannot actually function without one first thing in the morning. If you’re like me, then this investigation is definitely needed in your house so that you can ensure your kids are equipped with the best tea-making skills and have the best scientific knowledge to back up what makes a good cup of tea! This investigation looks at diffusion through the partially permeable membrane of a tea bag.

So firstly, we want to know what type of teabag makes the best drink?

Is it a square, a pyramid or a circle bag?

The activity involves using hot water, so adult supervision is essential.

Teabag diffusion

You’ll need

A stopwatch/timer

A piece of white paper

3 clear glass mugs (you are going to add hot water, so not thin ones that could crack)

Circle, triangle and pyramid tea bags

Thermometer or kettle

1. On the piece of white paper, draw a cross with a marker pen

2. Place one mug over the cross

3. Add the circle teabag

4. Boil water from the kettle and measure out 150ml (if you have a thermometer, you can improve reliability by keeping the temperature constant)

5. Pour over the teabag and start the stopwatch

6. Time how long it takes for the cross to disappear

7. Repeat with the pyramid and square teabag.

8. To make the investigation results more accurate, repeat with each teabag three times.

Record your results in a table

How does the tea diffuse into the water?

So which teabag was quicker?

You should find that the pyramid teabag was the quickest.

Why do you think this is?

As the water is added to the teabag, it causes the tea leaves to move and triggers diffusion of the leaves. Diffusion is defined as the movement of a substance from an area of higher concentration to an area of lower concentration. There are lots of tea molecules in the bag and none outside. The leaves themselves can’t pass through the bag, but their smaller particles containing colour and flavour can (the teabag itself acts as the partially permeable membrane). The addition of heat (from the hot water) to the tea bag causes its molecules to move much faster than at room temperature. This energy is more readily released in a shorter period of time than a tea bag filled with room temperature or cold water. The teabag shape affects the surface area and the pyramid due to its 3D shape providing more surface area for diffusion to take place and more area in the middle for the tea molecules to move around in spreading the colour and flavour.

Ok, so now they know which is the best teabag to use and how to let it brew…so I suggest you ask for a nice cuppa now!

Last Updated on February 23, 2023 by Emma Vanstone

Safety Notice

Science Sparks ( Wild Sparks Enterprises Ltd ) are not liable for the actions of activity of any person who uses the information in this resource or in any of the suggested further resources. Science Sparks assume no liability with regard to injuries or damage to property that may occur as a result of using the information and carrying out the practical activities contained in this resource or in any of the suggested further resources.

These activities are designed to be carried out by children working with a parent, guardian or other appropriate adult. The adult involved is fully responsible for ensuring that the activities are carried out safely.

Reader Interactions

January 06, 2012 at 8:20 pm

What a fun experiment. You always find ways to make the most ordinary things interesting. Thanks for sharing on Monday Madness.

January 06, 2012 at 9:43 pm

January 08, 2012 at 5:37 pm

Interesting especially since all my tea bags are rectangular. I don’t drink it a lot, but and getting to like it more and more. I haven’t tried many brands yet so I will have to start exploring it more. Fun exploration with the kids and I think they probably learned a lot about figuring things out on their own from it.

October 23, 2013 at 2:29 am

awesome job

February 17, 2014 at 8:32 pm

Jah hey thnx.i have learned smthng http://

February 23, 2014 at 12:40 am

Where did the square teabags come from? I have enjoyed tea in that shape but can’t recall what brand. Thanks!

April 29, 2014 at 7:13 pm

thanks! thats really helpful we’re doing a science project on how the shape of the tea bag affects the taste so that was really helpful!!

September 17, 2017 at 11:32 pm

Interesting and helpful. Thanks a lot. Although the cross takes a long time to remove for some reason. Wasnt sure in what marker to use though.

September 29, 2019 at 5:28 pm

WOW i love talking about tea irs so fun wowowowow i learnt science from tea omg wowowowowow omg tea is so interesting

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• Open access
• Published: 10 August 2024

RS-Dseg: semantic segmentation of high-resolution remote sensing images based on a diffusion model component with unsupervised pretraining

• Zheng Luo 1 ,
• Jianping Pan 1 , 2 , 3 ,
• Yong Hu 4 ,
• Lin Deng 4 ,
• Yimeng Li 1 ,
• Chen Qi 1 &
• Xunxun Wang 1  

Scientific Reports volume  14 , Article number:  18609 ( 2024 ) Cite this article

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Metrics details

• Computational science
• Computer science
• Engineering
• Environmental sciences
• Environmental social sciences

Semantic segmentation plays a crucial role in interpreting remote sensing images, especially in high-resolution scenarios where finer object details, complex spatial information and texture structures exist. To address the challenge of better extracting semantic information and ad-dressing class imbalance in multiclass segmentation, we propose utilizing diffusion models for remote sensing image semantic segmentation, along with a lightweight classification module based on a spatial-channel attention mechanism. Our approach incorporates unsupervised pretrained components with a classification module to accelerate model convergence. The diffusion model component, built on the UNet architecture, effectively captures multiscale features with rich contextual and edge information from images. The lightweight classification module, which leverages spatial-channel attention, focuses more efficiently on spatial-channel regions with significant feature information. We evaluated our approach using three publicly available datasets: Postdam, GID, and Five Billion Pixels. In the test of three datasets, our method achieved the best results. On the GID dataset, the overall accuracy was 96.99%, the mean IoU was 92.17%, and the mean F1 score was 95.83%. In the training phase, our model achieved good performance after only 30 training cycles. Compared with other models, our method reduces the number of parameters, improves the training speed, and has obvious performance advantages.

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MFCA-Net: a deep learning method for semantic segmentation of remote sensing images

An improved semantic segmentation algorithm for high-resolution remote sensing images based on DeepLabv3+

Joint superpixel and Transformer for high resolution remote sensing image classification

Introduction.

Semantic segmentation of remote sensing images classifies each pixel into different semantic categories. Compared with low-level features, semantic segmentation can directly obtain pixel-level semantic classes and is a crucial intermediate representation for remote sensing image understanding, which can promote intelligent analysis based on remote sensing images. Therefore, it has vital application value in fields such as land use planning, urban construction, and environmental monitoring 1 , 2 , 3 . With the development of remote sensing technology, images with higher spatial resolution can identify smaller and more object categories and details, increasing the seriousness of problems related to the same object with different spectra and different objects with the same spectrum. In multiclass semantic segmentation, an increase in the number of semantic classes will make the training samples of each class sparse, the difference between classes decrease, and the boundaries of ground objects become fuzzy, which brings great challenges to the training and reasoning of the model. How to extract semantic information effectively is the key to semantic segmentation. In an image, the semantic information of an object is usually determined by its local and global context. The model needs to be able to capture this contextual information to better understand the semantic relationships in the image 4 , 5 .

In recent years, the development of deep learning has provided reference experience for extracting information from remote sensing data. Compared with traditional methods, deep learning can better extract spectral-spatial features. Currently, many deep learning models, including classic convolutional neural networks and recently popular vision transformers (ViTs), have been successfully applied to remote sensing image segmentation 6 .

In semantic segmentation tasks, transformers are good at modeling global de-pendencies in images through attention mechanisms and can more accurately classify at the pixel level. However, the computational complexity is high, which is not conducive to processing high-resolution images 7 . CNNs have excellent local feature learning capabilities through convolution and pooling layers and can detect details. Techniques such as separable convolutions and dilated convolutions can also reduce computations. However, they lack representation for long-range pixel correlations and are weaker in extracting global contextual information 8 . Although Atrous Spatial Pyramid Pooling (ASPP) 9 improves this, there is a high computational complexity. Currently, almost all existing deep learning models use CNNs or transformers as a single network structure. The above problems cannot be solved, resulting in a poor segmentation effect. At the same time, to obtain higher accuracy, different modules are stacked and nested for reuse, which complicates the network and lengthens the training cycle. If a CNN and transformer are used together for semantic segmentation, then the segmentation effect will be greatly improved 10 . Therefore, how to combine the advantages of the two for remote sensing image segmentation is a problem worth considering. In recent years, denoising diffusion probabilistic models (DDPMs) have provided a more effective and reliable technical framework for image generation. It has become a vital method in the field of image generation. The diffusion model denoising module takes UNet as the main structure of the model and embeds the transformer self-attention mechanism, position coding and residual module. The self-attention mechanism can capture global information from the entire feature map. Position coding provides the location of the elements in the noise sequence feature to the model. The residual module can prevent the problem of gradient disappearance in the model when the network focuses on local information and can improve the generalization ability of the network. As excellent models for image generation, diffusion models can capture the semantic structure present in the data 11 . Its detail retention capability enables the generation of fine-grained highly detailed images, preserving the context and interrelationships of various objects in the image 12 . In recent years 13 , 14 , 15 , the diffusion model has also been shown to have strong potential in semantic segmentation. Therefore, we hope that this method can be applied to the task of remote sensing image interpretation.

Our goal is to make use of the powerful global information extraction and long-range dependency modeling capabilities of diffusion models to meet the challenges of semantic segmentation of high-resolution remote sensing images. We propose a pretrained feature extractor. The feature extractor is a diffusion model based on the UNet architecture. It is trained in an unsupervised manner on remote sensing images and can extract multiscale features of images. The pre-trained prior knowledge can help the model to achieve better semantic segmentation. These features are then used for semantic segmentation. For the corresponding classification module, we use a spatial-channel attention mechanism that can fuse multiscale features. Notably, it is lightweight, and only the parameters of the classification module are finetuned during segmentation training.

The main contributions of this paper are summarized as follows:

Diffusion models are used for remote sensing image semantic segmentation.

An unsupervised remote sensing image feature extraction model is created using diffusion model.

A lightweight classification module based on a spatial-channel attention mechanism is proposed for processing multiscale features.

Experiments prove that our proposed model and modules have good results.

Our paper is organized as follows: “ Related Work ” section discusses related work in recent years. “ Method ” section details the overall structure of our proposed method. “ Experimental detail ” section adds some details about the experiment. “ Main Experiment ”, “ Ablation experiments ” sections present and discuss the results of comparative experiment and ablation studies. “ Discussion ” section analyzes the conclusion and outlines future research directions. Appendix presents multiscale features.

Related work

Semantic segmentation of high-resolution remote sensing images.

Due to the uniqueness of high-resolution remote sensing images, traditional methods are not ideal for segmentation. In recent years, people have gradually introduced deep learning and made impressive progress. This is mainly due to the automatic feature extraction and end-to-end training of deep learning models. Specifically, the great success of convolutional neural networks in natural image processing has laid the foundation for deep learning in semantic segmentation. It also promoted convolutional network-based semantic segmentation models for remote sensing image analysis. Based on CNNs, researchers proposed the first end-to-end fully convolutional network (FCN) 16 , which replaces the fully connected layer with a convolutional layer to process arbitrarily sized images. Deng 17 fused the spectral index with the FCN model to improve the segmentation effect. Piramanayagam's 18 combination method based on random forest and FCN also achieved high segmentation accuracy. Li 19 used a nonuniform void convolutional network to extract high-level contour features of infrared image targets and fused high-level features of infrared images with detailed features of three scales of RGB images through fusion technology, thus enhancing the feature extraction capability of the network. Guan 20 designed a multiscale feature fusion module in an FCN and used superpixels to optimize the edge, which not only utilized spatial information but also improved the segmentation accuracy. The above model improves the ability of the model to recognize the edge information of the object and effectively integrates the feature information of the high and low layers of the image.Another mainstream framework, the Transformer, consists entirely of an attention mechanism and a feedforward neural network. HRNet 14 is a semantic segmentation network structure proposed by Microsoft Research in 2019 that integrates a transformer structure. The problem of feature resolution downsampling in a U-shaped network is solved, and the segmentation network can retain high-resolution detailed information. SegFormer 13 first used a transformer encoder to build a backbone in the field of semantic segmentation. The hybrid converter module combines convolution and self-attention with both local and global modeling capabilities.

Diffusion models for semantic segmentation

Diffusion models have roots in nonequilibrium thermodynamics 21 , initially concentrating on material diffusion during early research. Over time, their application has extended to diverse domains, including interpolation and prediction in time series data modeling. In the realm of waveform generation, WaveGrad 22 introduced a conditional model that estimates the gradient of data density. This model takes Gaussian white noise as input and iteratively refines input signals using a gradient-based sampler. Within natural language processing, numerous methods grounded in diffusion models have been developed for text generation. DiffuSeq 23 orchestrates diffusion processes in latent space, introducing a novel conditional diffusion model tailored for more intricate text-to-text generation tasks. In the field of computer vision, diffusion models stand as a vibrant area of research, with widespread applications such as image generation 24 , 25 , 26 , 27 , 28 , 29 , 30 , image super-resolution 31 , 32 , 33 , 34 , 35 , image restoration 36 , 37 , 38 , image editing 39 , 40 , and image-to-image translation 41 , 42 , 43 . Palette 44 employed a conditional diffusion model to establish a unified framework for four image generation tasks: colorization, inpainting, decropping, and JPEG restoration. By focusing on synthesizing images with specific desired styles 45 , image translation achieves conditional image generation via DDPM in iterative super-resolution (SR3) 46 . SR3 employs stochastic iterative denoising processes for super-resolution. Imagen Video 47 pioneers cascaded video diffusion models to generate high-definition videos, effectively transferring some methods proven in text-to-image generation tasks to video generation.Researchers have applied diffusion models in segmentation and classification with great potential 48 , 49 , 50 , 51 . Earlier works have focused on representations for zero-shot segmentation 52 or medical images. Wu 53 proposed MedSegDiff-V2, which combines UNet and transformers, outperforming medical image methods. Baranchuk 54 proved that the diffusion model can also be used for semantic segmentation, and the extracted features have rich semantic information. Some studies have explored detection and instance segmentation 26 , 32 .

As shown in Fig.  1 , the whole experiment process is divided into two stages: pre-training and pixel classification. As shown in the left part of Fig.  1 , during the pre-training phase, we input the image into the diffusion model. The diffusion model will degrade and reconstruct the image and learn the semantic information of the images. The most important purpose of pre-training is to enable the model to learn the semantic information of the image, which determines the quality of the features.After the training is completed, the weights of the diffusion model will be frozen, and the diffusion model becomes a feature extractor. We use the diffusion model to extract the image features. According to the set time steps, we will get the multi-scale features under different time steps. After these features are input into the classification module, the pixels are classified with the help of tags, and the result is obtained.We will cover these modules and their procedures in this “ Diffusion Model ” and “ Optimization of the Diffsuion Model ” sections introduce the diffusion and optimization process of the diffusion model. “ Diffusion Model Network Structure ” section shows the network structure of the Diffusion Model, and introduces how the diffusion model carries out feature extraction. “ Feature Extraction ” section introduces the process and details of feature extraction of remote sensing images using diffusion model. “ Lightweight Classification Module ” section The Classification Module dealing with multi-scale features is introduced.

The overall training process of the RS-Dseg.

Diffusion model

Forward diffusion is a forward Markov chain diffusion process that uses a Gaussian noise model. As shown in Fig.  2 , in this process, given the real image \({x}_{0}\) , Gaussian noise with variance \({\beta }_{t}\) is continuously added at a given step, resulting in a noise sequence: \({x}_{1},{x}_{2}\) … After adding enough noise, the image is completely corrupted.

Diagram of the forward and reverse processes.

Its probability distribution is of the form:

The joint distribution of \({x}_{1:T}\) given \({x}_{0}\) is as follows:

If we define \({\alpha }_{t}=1-{\beta }_{t}\) , then \({\prod }_{t=1}^{T}{\alpha }_{i}\) is written for \({\overline{\alpha }}_{t}\) , \({\overline{\alpha }}_{t}\) is the hyperparameter set by the Noise schedule, and (1) can be transformed as follows:

Therefore, Eq. ( 4 ) shows that the value of \({x}_{t}\) depends on the original image \({x}_{0}\) and the random noise \(\epsilon\) . In other words, from the initial value \({x}_{0}\) and the diffusion rate at each step, we can obtain \({x}_{t}\) at any time. When \(t\to \infty\) , \({\beta }_{t}\) continues to increase, and \({\overline{\alpha }}_{t}\) gradually decreases. The mean and variance of \(\epsilon\) are 0 and 1, respectively. Finally, \({x}_{t}\) is an isotropic Gaussian distribution.

The reverse process is the denoising process. we need to learn a model \({p}_{\theta }\) to approximate these conditional probabilities in order to run the reverse diffusion process:

Therefore, the derivation of the following formula is mainly to solve for the mean value. When the variance is given, the Gaussian distribution function of the specified distribution mode can be obtained by solving the mean to simulate the image. Therefore, the derivation of the following formula is used to determine the variance. A standard Gaussian noisy image \({x}_{t}\) is generated from the prior distribution, and then the noise is gradually removed from it by running a learnable backward running Markov chain. The posterior probability of the forward process can be expressed as follows:

According to the Bayes formula and the normal distribution property of Gaussian noise, we can obtain the following:

Therefore, we obtain the a posteriori formula with the parameter \({\alpha }_{t}\) :

Optimization of the diffsuion model

In the forward process, we use randomly generated noise to degrade the image. In the reverse process, we use the corrupted image to estimate the distribution of noise and expect the predicted noise to be close to the real noise. This process can be realized through the iterative training of a neural network. The mathematical principle is to maximize the log-likelihood of the model's predicted distribution, optimizing the cross-entropy between the true distribution \({x}_{0}\) and the predicted distribution \(q\left({x}_{0}\right)\) ,we can obtain the following with Eq. ( 5 ):

We set \({\Sigma }_{\theta }({x}_{t},t)={\sigma }_{t}^{2}I\) , \({\sigma }_{t}^{2}={\widetilde{\beta }}_{t}=\frac{1-{\overline{\alpha }}_{t-1}}{1-{\overline{\alpha }}_{t}}{\beta }_{t}\) , we can obtain the following with Eq. ( 4 ):

where C is a constant that does not depend on θ。From Eq. ( 4 ), we deduce that \({x}_{0}=\frac{{x}_{t}-\sqrt{1-{\overline{a} }_{t}}{\varvec{\epsilon}}}{\sqrt{{\overline{a} }_{t}}}\) . According to the standard Gaussian density function, the mean and variance can be parameterized as follows:

Therefore, at this point, in the backward process, to compute \({\mu }_{t}\) , we need to compute \({x}_{t}\) and \({{\varvec{\epsilon}}}_{t}\) . Therefore, we need to train a neural network to predict the distribution of \({{\varvec{\epsilon}}}_{t}\) . The mean \({\mu }_{t}\) is obtained by predicting the Gaussian noise \({{\varvec{\epsilon}}}_{\theta }\left({x}_{t},t\right)\) from \({x}_{t}\) and \(t\) for each time step. By solving the KL divergence of the multivariate Gaussian distribution, and bring Eq. ( 8 ) into Eq. ( 11 ), we can obtain the following:

Empirically, Ho 25  found that training the diffusion model works better with a simplified objective that ignores the weighting term:

The whole process is to take the input \({x}_{0}\) from \(1\dots T.\) We randomly sample a T in \(t\) . The noise \(\epsilon_{t} \sim {\mathcal{N}}\left( {0,{\varvec{I}}} \right)\) is then sampled from the standard Gaussian distribution. Finally, the objective function \({L}_{t-1}^{\text{simple}}\) is minimized.

As shown in Fig.  2 , the remote sensing images are input into the denoising model as \({x}_{0}\) , and the model is allowed to add noise and reconstruct these remote sensing images. In this way, the distributional noise of the semantic information of these pictures is learned to predict \({\mu }_{t}\) . The whole process does not require labels, and unsupervised pretraining is performed. After completing this process, the denoising model freezes the network parameters. It is subsequently used for feature extraction.

Diffusion model network structure

Figure  3 shows the network structure of the Denoiser. It has a U-shaped structure similar to that of the UNet network. It mainly includes a decoder, an intermediate layer and an encoder. The denoiser mainly consists of a series of Basic I blocks and Basic blocks. At the same time, we connect the encoder to the decoder through the jump connection layer. The denoiser includes the residual structure of the CNN and the self-attention mechanism of the transformer. As shown in Fig.  4 , we used three images with different noise levels as input for model training.

Diffusion model network structure.

Feature extraction and processing.

To help the model better understand and handle the sequence data, we added positional encoding first, as shown in Fig.  3 . This generates a positional code for the noise sequence data, introducing location information into sequence data. Position coding is generated by sine and cosine functions and is related to noise levels. Given the input noise level \(N\_L\) , the dimension of the position encoding is \(dim\) , and \(dim\) is half of the input dimension. First, the position vector \({step}_{i}\) is computed to generate a sequence whose values are in the range [0, 1), representing the proportion of each position relative to the entire sequence. This sequence is mapped to a new range using an exponential function, ensuring large differences in encoding at different locations:

where i represents the position in the sequence and k represents the index in the position vector. \(encodin{g}_{i,j}\) represents the components of each position in the position coding matrix. The sine and cosine values are then concatenated to form the final positional coding vector. At the very beginning of each stage, we add a linear affine layer to embed location information for the feature representation of the network, which helps the model better understand and obtain semantic information for different locations. The Basic I block and Basic block are the main components of the network. The Basic block consists of a GroupNorm (GN) layer, a swish activation function, a dropout layer, and a convolution layer. Basic I replaced the dropout layer with an identity map. We replaced the BatchNorm layer with a GN layer to reduce the impact of BatchSize on the model. The Swish activation function adds nonlinearity to the network, and in deep models, it works better than ReLU 55 . In the first stage of the encoder, to avoid losing considerable semantic information in the initial stage, we do not downsample after the two Basic blocks and Basic I blocks. In other stages, subsampling or attention mechanisms are added at the end. For the input image \(X\in {\mathbb{R}}^{H\times W\times 3}\) , after the first stage, the output is \({F}_{1}^{d}\in {\mathbb{R}}^{H\times W\times 128}\) . From the second stage to the fourth stage, after downsampling at the end, the height and width of the feature are reduced by half, the number of channels is doubled, and the output is \({F}_{n}^{d}\in {\mathbb{R}}^{\left(\frac{H}{{2}^{n}}\right)\times \left(\frac{W}{{2}^{n}}\right)\times \left(128\times n\right)},n=\text{2,3},4\) . In the fifth stage, we do not increase the number of channels of the feature because we consider that when making a jump connection with the feature of the decoder, we increase the number of channels in the second dimension of the feature. The final output of the encoder is \({F}_{5}^{d}\in {\mathbb{R}}^{\left(\frac{H}{16}\right)\times \left(\frac{W}{16}\right)\times 1024}\) .However, the encoder does not increase the number of characteristic channels in stage 5. In the middle layer, the model adjusts \({F}_{5}^{d}\) by a self-attention mechanism.The following is an additional feature representation for the next stage of the decoder:

Then, Q, K, and V can be calculated via convolution:

where \(i\) and \(j\) represent row \(i\) and column \(j\) of \(norm\) , respectively; ( \(m\) , \(n\) ) and K is row \(m\) and column \(n\) of the convolution kernel; and k and l are the height and width of the convolution kernel, respectively. With the Chunk function, Q, K, and V can be obtained. Then, the attention score is calculated:

Finally, the weighted sum of V is calculated using the attention weight:

The output of the middle layer can be expressed as \(F^{m} \in {\mathbb{R}}^{{\left( \frac{H}{16} \right) \times \left( \frac{w}{16} \right) \times 1024}}\) .As shown in Fig.  3 , before the start of each stage of the decoder, the characteristics of each stage of the encoder are combined with the output of the previous stage of the decoder through a jump connection as the input of the next stage of the decoder, \(x_{1} \in {\mathbb{R}}^{{\left( \frac{H}{16} \right) \times \left( \frac{W}{16} \right) \times 2048}}\) .

By connecting the feature graphs of the upsampling and downsampling paths, the low and high-level feature information is combined. This connection helps to retain richer image details and contextual information, avoiding the problem of information loss or disappearing gradients. Information fusion enables the network to focus on both local and global information, improves the model's ability to understand each part of the image, and thus improves the performance of image processing tasks. In addition, the connection feature also helps to introduce more spatial details in the upsampling process, improving the quality of the reconstructed image and the ability to retain details. In the decoder, there are many self-attention mechanisms. This helps the model better understand the semantic information between different features in the noise sequence. The upsampling at the end of each stage causes the size of the feature to become twice the size of the input, gradually returning to the original size of the input.

Feature extraction

After pretraining, the network learns the noise distribution in different diffusion stages. In the reverse stage, the images of different noise levels generated in the forward stage are simulated by the random distribution of Gaussian noise. The whole process does not need backpropagation; it is the result of pretraining. We believe that the generated noise sequence images, due to the powerful global information extraction and remote dependence modeling capabilities of the diffusion model, highlight the importance of different ground objects, thereby helping the model better understand the differences between the various categories. The feature extraction of these images, which contain rich semantic information, helps the network classify pixels better. This is shown in the Appendix.

When performing semantic segmentation, in the reverse process, the feature extractor based on the UNet architecture can perform feature extraction. Figure  4 shows that we feed the trained samples into the denoising model and set the diffusion step in advance. The model gradually diffuses the initial Gaussian noise distribution to approximate the distribution of the sample image. The encoder in the denoising model extracts the image features with the setted diffusion steps. Wele 56 and Dmitry 54 reached a good conclusion on determining diffusion time. Therefore, we directly set the time t to [50, 100, 400]. Figure  4 and the Figure A.1 in Appendix show that images containing different noise levels have different scales of semantic information. The largest difference between the image with diffusion step t = 50 and the other two images is to distinguish the road from the other ground classes in the image. The diffusion step, t = 100, distinguishes natural from unnatural features. T = 400, contains the semantic information of each ground class. The combination of these features can ensure the segmentation accuracy of different land classes. In the Appendix A.1, we show the extracted multiscale features.

Using diffusion models to extract multiscale features is an important process. In the subsequent processing, we directly use these features for classification. Therefore, the extraction and selection of features largely determine the segmentation results. In the ablation experiment, we used different numbers of features for comparison experiments.

Lightweight classification module

For the extracted multiscale features, the proposed classification module needs to classify and upsample them to obtain the results. These features come from images with 3 noise levels and contain different scales and channel numbers, so these features need to be fused and screened. We connect features through convolutional layers and then filter the features through attention mechanisms. Spatial attention and channel attention are weights that adaptively recalibrate features based on context dependencies. Compared with other methods, this method has fewer parameters and lower computational complexity. As shown in Fig.  5 , in addition to the attention mechanism, the whole module contains a small amount of convolution and upsampling, and few parameters are learned. This significantly improves the training speed of the model. and it will be discussed later in the ablation experiment. The experimental results also prove that spatial and channel attention mechanisms are more suitable for feature screening than other methods.

Classification module.

Since the features come from three noise levels, the features of the same size at different noise levels need to first be joined together by convolution. The input features go through a convolutional layer and then enter the channel compression and attention extraction module. In this module, multichannel multiscale features are excited because of their importance. As Fig.  5 shows, this attention module includes spatial squeeze and channel excitation (SSCE) and channel excitation and spatial squeeze (CSSE) 57 .

The SSCE module squeezes the features spatially through fully connected layers and sigmoid activation to obtain excitation weights. The weights are multiplied by the feature scales of each channel's features to capture the importance of different channel features. The extracted multiscale features can be defined as \(F\in {\mathbb{R}}^{H\times W\times C}\) . In SSCE, features are first squeezed spatially through a global pooling layer:

This operation compacts the global spatial information to \({f}_{1}\in {\mathbb{R}}^{1\times 1\times C}\) and then passes through two convolution layers and activation functions:

where \({f}_{2}\in {\mathbb{R}}^{1\times 1\times \left(\frac{C}{2}\right)}\) is the feature after the first convolution and \({f}_{3}\in {\mathbb{R}}^{1\times 1\times C}\) is the feature after two convolutions. Two convolution operations recalibrate the feature on the channel, activating the importance of the different channels after passing through the sigmoid activation function. \({f}_{3}\) acts as a scaling factor to activate the importance of the channel in the original feature:

\({F}_{1}\) represents the features that are given different channel importance after passing through the SSCE module. The CSSE module squeezes the features channelwise using convolutional layers and sigmoid activation to obtain excitation weights. Multiplying the weights with features at different spatial locations allows the capturing of spatial importance. Similarly, for the original feature \(F\in {\mathbb{R}}^{H\times W\times C}\) , passing it through a convolution layer and activation function can be expressed as follows:

\({f}^{1}\in {\mathbb{R}}^{H\times W\times 1}\) , which represents the importance of different spatial locations. \({f}^{1}\) will then act as a scaling factor to rescale the importance of different spatial locations in the original feature:

\({F}_{2}\) represents features that are given different spatial location importance after passing through the CSSE module. Finally, by adding \({F}_{1}\) and \({F}_{2}\) , the treated feature F^* is obtained. As shown in the Fig.  6 , these features that are excited in the channel and spatial positions are used for the final classification convolution and upsampling, and finally, the segmented semantic result is obtained.

Two-branch attention mechanism (SSCE and CSSE).

Experimental detail

Experimental settings.

All experiments were conducted on a 64-bit Windows system equipped with an NVIDIA GeForce RTX 4090 24G GPU. In our experimentation, we first conducted unsupervised pretraining on the training and validation images, excluding the labels, to obtain the feature extractor. During pretraining, we employed the mean squared error (MSE) loss function, with a batch size of 4 and a learning rate of 0.00001. The optimizer utilized was AdamW, with an exponential moving average (EMA) coefficient of 0.9999. In the classification stage, we fixed the batch size at 8 and employed the SGD optimizer with a learning rate of 0.0001.

Evaluation metrics

Accuracy evaluation. We begin from the overall and category perspectives. For the whole, we use the accuracy, average F1 score (Ave.F1), and average IoU (MIoU). These parameters can be represented by the following formula:

Additionally, the F1_score, IoU, recall, and precision are calculated separately for each class. They can be represented by the following formula:

where k is the number of categories.

Postdam : The urban scene classification dataset is provided by ISPRS 66 . The scene is located in the Postdam, which has large buildings, narrow streets and dense settlement structures. The entire dataset contains 6 categories: impervious surfaces, buildings, vegetation, trees, cars, and background. The dataset contains 38 remote sensing images. Among them, 24 images are the training set and 14 images are the test set. We divided the training and validation sets with 20:4 among the 24 remote sensing images. We resize the image to 5120 by 5120 and crop it to 256 by 256. The final dataset has a ratio of 8000:1600:5600. The final quantity ratio is 8000:1600:5600. The RGB values for each category are presented in Table 1 .

GID : The first dataset used in our research is the GID land cover dataset 58 from Wuhan University, which was captured by the Gaofen-2 (GF-2) satellite. We specifically utilized fine classification samples, which consisted of 15 labeled categories. The RGB values for each category are presented in Table 2 and are visualized according to the provided specifications. The dataset comprises 10 processed images with dimensions of 7200 × 6800, which we cropped into 256 × 256 patches.

Main experiment

We compared our model against FCN, ConvNeXt-v2, HRNet, DeepLabv3 + , UNet, SegNeXt, Segformer and FTransUNet. FCN is the first work of deep learning for semantic segmentation and can be adapted to any size of input. Deeplabv3 + and UNet represent early convolutional attention networks. The model can improve the use of features at different levels. The SegFormer model represents another structure transformer, focusing more on the overall message. ConvNeXt-v2 and SegNeXt are novel convolutional attention networks proposed in recent years. Through the information exchange of different branches, HRNet supplements the information loss caused by the reduction in the number of channels. FTransUNet is proposed to provide a robust and effective multimodal fusion backbone for semantic segmentation by integrating both CNN and Vit into one unified fusion framework. The parameters of each model we used in the experiments are shown in Table 3 . Under the premise of ensuring excellent experimental results, our classification head greatly reduces the experimental parameters compared with other models. A decrease in the number of experimental parameters can increase the training speed.

As shown in a and b of Fig.  7 , we recorded the loss values of the seven models on the two datasets over 80 training cycles. Our model (the blue curve) has a lower loss value at the beginning of training than do the other models. It gradually reaches the optimal parameters after approximately 30 training cycles. Other models require approximately 50–60 cycles. The training speed of our model is the fastest among these models.

Loss curve. ( a ): Postdam dataset. ( b ): GID dataset.

Results on the postdam dataset

Table 4 lists the numerical results for each semantic segmentation method. The results show that the proposed RS-Dseg method is superior to other methods in terms of accuracy, MIoU and Ave.F1. UNet with an extended convolutional Deeplabv3 + and decent-encoder structure obtains global context information by extending the receptive field. The experimental results show that a SegFormer with a self-attention mechanism is inferior to our model in long-term dependence modeling. DeepLabv3 + with cavity convolution and residual connections and UNet with a ResNet101 skeleton achieve good results, both reaching 97%. HRNet ranks first among the other models. Compared with HRNet, our method improves the accuracy by 0.68%, the MIoU by 1.83%, and the Ave. F1 by 0.98%. Our approach exceeds the average of the other networks in all three indices by 2.22%, 5.88% and 3.54%. As shown in the third and fourth row of Fig.  8 , Our method is able to extract the edges of features more finely, which is validated in our MIoU values. At the same time, our method also shows a good ability to distinguish similar Categories.

Examples of semantic segmentation results on the Postdam dataset. ( a ): Ours. ( b ): FCN. ( c ): ConvNeXt-v2. ( d ): HRNet. ( e ): Deeplabv3+. ( f ): UNet. ( g ): SegNeXt. ( h ): SegFormer. ( i ): FTransUNet.

Results on GID dataset

Table 5 lists the numerical results for each semantic segmentation method. The results show that the proposed RS-Dseg method is superior to other methods in terms of accuracy, MIoU and Ave.F1. At the same time, with respect to the three indices, the proposed method exceeds the average level of the other models. Our method achieves a 97.00% accuracy, exceeding that of the UNet model by 1.11%. With regard to MIoU, our model achieves a good score of 92.17%, an improvement of 1.48% compared to UNet. With regard to Ave.F1, our model demonstrates an improvement of 0.79% compared to UNet. Neither the transformer-based SegFormer and FTransUNet nor ConvNeXt-v2 and SegNeXt of the new convolutional networks perform as well as our method in this paper.

Compared to other semantic segmentation methods, our proposed model significantly improves object edge identification and integrity. Evaluations show that it increases the average IoU by 5% over others, demonstrating an advantage in accurately extracting edges. Figure 9 illustrates our model and other methods for segmenting the same image. Our model segments class boundaries more precisely, while others exhibit edge blurring. We attribute this to the multiscale features and channel-spatial attention modules. The Attention Mechanism enhances the representation of edge features by scaling the spatial and channel importance of the features. Additionally, our model achieves better object integrity, avoiding fragmentation. For instance, it greatly improves road segmentation accuracy. Compared to other methods, our approach more accurately maintains the coherence and integrity of edges. Future work will further enhance the representation of detailed object edges. For per-class segmentation, we evaluate result using IoU, F1, Precision and Recall. Table 6 shows that, except for artificial grasslands and shrubs, our model outperforms the other models for most of the other classes on most of the metrics. The bolded numbers in-dicate the highest scores among the seven models for that metric and class.

Examples of semantic segmentation results on the GID dataset. ( a ): Ours. ( b ): FCN. ( c ): ConvNeXt-v2. ( d ): HRNet. ( e ): Deeplabv3+. ( f ): UNet. ( g ): SegNeXt. ( h ): SegFormer. ( i ): FTransUNet.

To facilitate a more effective comparison, we choose seven common categories from a total of 15 land classes. Table 7 presents the IoU performance of different methods across these seven categories. Our model consistently outperforms the other models in each category. The average IoU reaches a satisfactorily high score of 93.04%. In comparison, HRNet and UNet, among other models, also achieve average IoU values exceeding 90%. Notably, our model surpasses UNet by approximately 1.7% in average IoU. For example, in the “Transportation” category, other models achieve a minimum IoU of only 64.91%, with the highest reaching 85.22%. Our model surpasses this highest value by 4%. This suggests that in the task of segmenting elongated features, our model demonstrates notably superior performance.

Ablation experiments

In this part, we first discuss the selection of the number of features. The model requires sampling the image through various diffusion steps, and in the decoder of the diffusion model, the size of the features doubles after each upsampling stage. Consequently, a multitude of features can be extracted from a single image. Generally, the more features available, the more beneficial it is for segmenting the model. However, an increase in the number of features results in a higher number of network parameters, thereby escalating the training burden. Hence, the number of features needs careful consideration. Subsequently, we empirically validate each module in this approach, particularly focusing on assessing the effectiveness of DDPM and the space-channel attention mechanism (SCAM).

Different numbers of features

First, we experiment with different numbers of selected features. As mentioned in “Feature Extraction” of “Method”, these features originate from the noise diffusion process. We set diffusion steps so that features of varying scales can be extracted at each step. The number of selected features also determines the classification module parameters. To balance suitable feature numbers and the parameter of classification modules, we tested 4, 5 and 6 feature sets.

According to Table 8 , as the number of features increases, the number of parameters also increases by millions. The average change in accuracy is approximately 0.03%. The MIoU increases by approximately 1% with 6 features, and the Ave.F1 is greater than 96%. The parameter count also increases to approximately fifty-two million. Considering all factors, we selected 4 features for subsequent comparative experiments.As shown in Figure 10 , the segmentation results reveal a few intraclass inconsistencies when using only four features. However, these inconsistencies vanish almost entirely as the number of features increases to six. Despite the differences in categorical consistency, the segmentation boundary and connectivity remain largely unaffected by the number of features. This demonstrates the model's robustness in preserving boundary delineation and connected components with varying feature dimensions. By incrementally enriching the feature space, categorical cohesion improves without compromising the integrity of spatial segmentation. The model thus strikes an effective balance between semantic and structural consistency as feature information increases.

Examples of semantic segmentation results of 4, 5, and 6 features: ( a ) four features; ( b ) five features and ( c ) six features.

Efficiency of moudles

First, we remove two-branch attention mechanism (TBAM)from the classification module and use only DDPM and a small amount of convolution as the model for the first experiment (DDPM). Then, we replace TBAM with ASPP in the classification module, and use DDPM+ASPP as the model of the second experiment to verify whether the features extracted by the feature extractor still need to be further encoded. In addition, to supplement the performance of verifying TBAM, we choose to introduce residual blocks. We selected ResNet-50 as the main model for training, and in another set of experiments, SCAM is added to the end of ResNet-50. We compare the results of the above four sets of experiments with our RS-Dseg.

Table 9 shows the results of each experiment, and our method has the best performance. The performance of the ResNet-50 model has been improved after the addition of SCAM. DDPM results exceeded ResNet and ResNet + SCAM and are slightly lower than DDPM+ SCAM, achieving an Accuracy of 95.14%. Compared with other models in Table 3 , the results of DDPM are also above average, indicating that DDPM is competent for the segmentation task.In addition, following the substitution of SCAM with ASPP, there is a dramatic drop in the performance of model, even performing worse than ResNet-50. DDPM performs worse than DDPM+ SCAM, but better than DDPM+ASPP. This is because the features extracted by the feature extractor can essentially be used for pixel classification without requiring further coding. Additional encoding would degrade segmentation quality. Moreover, from part A.1 of the Appendix, it's evident that these features have been able to represent the categories of ground objects on different scales

Figure 11 shows the result of the segmentation. The combination of (a) gets the best result. Thanks to the feature extractor, these two methods can get accurate results. While (c) is the result of DDPM+ASPP, it can be seen that the quality of segmentation is poor. Using ASPP to re-encode features results in a lot of information being lost, which reduces the accuracy of segmentation. (d) Compared with (e), ResNet-50+ TBAM can better maintain the coherence of long and narrow features due to the presence of space-channel modules.

Examples of semantic segmentation results on the GID dataset ( a ): DDPM + TBAM. ( b ): DDPM. ( c ): DDPM + ASPP. ( d ): ResNet-50. ( e ): ResNet-50 + TBAM.

In this paper, we explore the application of diffusion models in the semantic segmentation of high-resolution remote sensing images, leading to the simplification of existing models for this task. We introduce a lightweight classification module based on spatial-channel attention mechanisms, which enables rapid semantic segmentation by utilizing multiscale features from a pretrained diffusion model. Our experimental results demonstrate that the feature extractor of the unsupervised pretrained diffusion model effectively extracts multiscale features with contextual information. This is due to a prior knowledge of the diffusion model. The lightweight classification module efficiently fuses these features and performs semantic segmentation, significantly reducing the training cycle. These findings highlight the potential of applying diffusion models to remote sensing image semantic segmentation, which can achieve optimal performance compared to current methods. Importantly, in our research, we use labeled images during the segmentation stage but do not utilize them for feature extraction in the diffusion model. In future work, exploring how to leverage the feature extraction capabilities of diffusion models for unsupervised or semi-supervised classification would be valuable.

Data availability

All data generated or analysed during this study are included in this published article [and its supplementary information files].

Karra, K., Kontgis, C., Statman-Weil, Z., Mazzariello, J. C., Mathis, M. & Brumby, S. P. Global land use/land cover with Sentinel 2 and deep learning. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS 4704–4707 (2021) https://doi.org/10.1109/IGARSS47720.2021.9553499

Chen, W., Wu, A. N. & Biljecki, F. Classification of urban morphology with deep learning: Application on urban vitality. Comput. Environ. Urban Syst. 90 , 101706 (2021).

Article   Google Scholar  

Yuan, Q. et al. Deep learning in environmental remote sensing: Achievements and challenges. Remote Sens. Environ. 241 , 111716 (2020).

Ming, D., Luo, J.-C., Shen, Z., Wang, M. & Sheng, H. Research on high resolution remote sensing image information extraction and target recognition. Sci. Surv. Mapp. 3 , 18–20+3 (2005).

Google Scholar  

Yan, Ma. & Kizirbek, G. Research review on image semantic segmentation in high-resolution remote sensing image interpretation. Explor. Comput. Sci. Technol. 17 (07), 1526–1548 (2023).

Vaswani, A. et al . Attention is all you need. In Proceedings of Advances in Neural Information Processing Systems 5998–6008 (2017).

Han, K., Wang, Y., Chen, H., et al . A survey on visual transformer. Preprint arXiv:2012.12556 (2020).

Elngar, A. A. et al. Image classification based on CNN: A survey. J. Cybersecur. Inf. Manag. 6 (1), 18–50 (2021).

Chen, L.-C. et al . Rethinking atrous convolution for semantic image segmentation. Preprint arXiv:1706.05587 (2017).

Yangyi, D., He Kang, Hu. & Qi, H. K. A review of CNN-transformer hybrid model in the field of computer vision. Model. Simul. 12 (4), 3657–3672 (2023).

Valanarasu, J. M. J., Oza, P., Hacihaliloglu, I. et al . Medical transformer: Gated axial-attention for medical image segmentation. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24 . Springer, 36–46 (2021).

Wang, H., Cao, J., Anwer, R. M. et al . Dformer: Diffusion-guided transformer for universal image segmentation. Preprint arXiv:2306.03437 (2023).

Xie, E. et al. SegFormer: Simple and efficient design for semantic segmentation with transformers. Adv. Neural Inf. Process. Syst. 34 , 12077–12090 (2021).

Jingdong, W. et al. Deep high-resolution representation learning for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 43 , 3349–3364 (2020).

Roy, A. G., Navab, N., & Wachinger, C. Concurrent spatial and channel ‘squeeze & excitation’in fully convolutional networks. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part I , 421–429 (2018).

Long, J., Shelhamer, E., & Darrell, T. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition 3431–3440 (2015).

Deng, G. H., Gao, F., Luo, Z. P. Research on semantic segmentation of high- resolution remote sensing data based on improved fully convolution neural network. In 4th China High Resolution Earth Observation Conference, Wuhan 1125–1137 (2017).

Piramanayagam, S. et al. Classification of remote sensed images using random forests and deep learning framework. In Image and signal processing for remote sensing XXII Vol. 10004 (SPIE, 2016).

Li, B.-Q. et al. Asymmetric parallel semantic segmentation model based on full convolutional neural network. Acta Electron. Sinica 47 (7), 1058 (2019).

Shen-ke, G. U. A. N. et al. A semantic segmentation algorithm using multi-scale feature fusion with combination of superpixel segmentation. J. Graph. 42 (3), 406 (2021).

Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., & Ganguli, S. Deep unsupervised learning using non-equilibrium thermodynamics. In Proceedings of ICML 2256–2265 (2015).

Nanxin, C. et al . Wavegrad: Estimating gradients for waveform generation. Preprint arXiv:2009.00713 (2020).

Gong, S. et al . DiffuSeq: Sequence to sequence text generation with diffusion models. In The Eleventh International Conference on Learning Representations . (2022).

Sinha, A., Song, J., Meng, C. & Ermon, S. D2C: Diffusion decoding models for few-shot conditional generation. Adv. Neural Inf. Process. Syst. 34 , 12533–12548 (2021).

Ho, J., Jain, A. & Abbeel, P. Denoising diffusion probabilistic models. Adv. Neural Inf. Process. Syst. 33 , 6840–6851 (2020).

Song, Y. & Ermon, S. Generative modeling by estimating gradients of the data distribution. Adv. Neural Inf. Process. Syst. 32 , 11918–11930 (2019).

Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., & Poole, B. Score-based generative modeling through stochastic differential equations. Advances in neural information processing systems, (2021).

Dhariwal, P. & Nichol, A. Diffusion models beat GANs on image synthesis. Adv. Neural Inf. Process. Syst. 34 , 8780–8794 (2021).

Nichol, A. Q. & Dhariwal, P. Improved denoising diffusion probabilistic models. In Proceedings of ICML , 8162–8171 (2021).

Song, J., Meng, C., & Ermon, S. Denoising diffusion implicit models. In International Conference on Learning Representations (2021).

Daniels, M., Maunu, T. & Hand, P. Score-based generative neural networks for large-scale optimal transport. Adv. Neural Inf. Process. Syst. 34 , 12955–12965 (2021).

Chung, H., Sim, B., & Ye, J. C. Come-closer-diffuse-faster: Accelerating conditional diffusion models for inverse problems through stochastic contraction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 12413–12422 (2022).

Kawar, B., Elad, M., Ermon, S. & Song, J. Denoising diffusion restoration models. Adv. Neural Inf. Process. Syst. 35 , 23593–23606 (2022).

Esser, P., Rombach, R., Blattmann, A. & Ommer, B. ImageBART: Bidirectional context with multinomial diffusion for autoregressive image synthesis. Adv. Neural Inf. Process. Syst. 34 , 3518–3532 (2021).

Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., & Van Gool L. RePaint: Inpainting using denoising diffusion probabilistic models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 11461–11471 (2022).

Jing, B., Corso, G., Berlinghieri, R., & Jaakkola, T. Subspace diffusion generative models. Preprint arXiv:2205.01490 (2022).

Avrahami, O., Lischinski, D., & Fried, O. Blended diffusion for text-driven editing of natural images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 18208–18218 (2022).

Choi, J., Kim, S., Jeong, Y., Gwon, Y., & Yoon, S. ILVR: Conditioning method for denoising diffusion probabilistic models. In IEEE/CVF International Conference on Computer Vision 14347–14356 (2021).

Meng, C., Song, Y., Song, J., Wu, J., Zhu, J.-Y., & Ermon, S. SDEdit: Guided image synthesis and editing with stochastic differential equations. In International Conference on Learning Representations (2021).

Zhao, M., Bao, F., Li, C. & Zhu, J. EGSDE: Unpaired image-to-image translation via energy-guided stochastic differential equations. Adv. Neural Inf. Process. Syst. 35 , 3609–3623 (2022).

Wang, T., Zhang, T., Zhang, B., Ouyang, H., Chen, D., Chen, Q., & Wen, F. Pretraining is all you need for image-to-image translation. Preprint arXiv:2205.12952 (2022).

Li, B., Xue, K., Liu, B., & Lai, Y.-K. VQBB: image-to-image translation with vector quantized brownian bridge. Preprint arXiv:2205.07680 (2022).

Wolleb, J., Sandkühler, R., Bieder, F., & Cattin, P. C. The Swiss Army knife for image-to-image translation: Multi-task diffusion models. Preprint arXiv:2204.02641 (2022).

Saharia, C., Chan, W., Chang, H., Lee, C., Ho, J., Salimans, T., Fleet, D., & Norouzi, M. Palette: Image-to-image diffusion models. In ACM SIGGRAPH 2022 Conference Proceedings 1–10 (2022).

Sasaki, H., Willcocks, C. G., & Breckon, T. P. UNIT-DDPM: UN-paired image translation with denoising diffusion probabilistic models. Preprint arXiv:2104.05358 (2021).

Chitwan, S. et al. Image super-resolution via iterative refinement. IEEE Trans. Pattern Anal. Mach. Intell. 45 , 4713–4726 (2022).

Ho, J., Chan, W., Saharia, C., Whang, J., Gao, R., Gritsenko, A., Kingma, D. P., Poole, B., Norouzi, M., Fleet, D. J. et al . Imagen video: High definition video generation with diffusion models. Preprint arXiv:2210.02303 (2022).

Brempong, E. A. et al . Denoising pretraining for semantic segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition 4175–4186 (2022).

Chen, S., Sun, P., Song, Y., & Luo, P. Diffusiondet: Diffusion model for object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision 19830–19843 (2023).

Chen, T., et al . A generalist framework for panoptic segmentation of images and videos. In Proceedings of the IEEE/CVF International Conference on Computer Vision 909–919 (2023).

Gu, Z. et al . Diffusioninst: Diffusion model for instance segmentation. Preprint arXiv:2212.02773 (2022).

Burgert, R. et al . Peekaboo: Text to image diffusion models are zero-shot segmentors. Preprint arXiv:2211.13224 (2022).

Wu, J. et al. MedSegDiff: Medical image segmentation with diffusion probabilistic model. In Medical Imaging with Deep Learning (PMLR, 2023).

Baranchuk, D. et al . Label-efficient semantic segmentation with diffusion models. In International Conference on Learning Representations (2021).

Wu, Y., & He, K. Group normalization. In Proceedings of the European conference on computer vision (ECCV) (2018).

Bandara, W. G. C., Nair N. G., Patel, V. M. Remote sensing change detection using denoising diffusion probabilistic models. e-prints arXiv:2206.11892 (2022).

Roy, A. G., Navab, N., Wachinger, C. Concurrent spatial and channel ‘squeeze & excitation’in fully convolutional networks. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16–20, 2018, Proceedings, Part I 421–429 (2018).

Tong, X.-Y. et al. Land-cover classification with high-resolution remote sensing images using transferable deep models. Remote Sens .Environ. 237 , 111–322 (2020).

Long, J., Shelhamer, E., Darrell, T. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition 3431–3440 (2015).

Woo, S., Debnath, S., Hu, R. et al . Convnext v2: Co-designing and scaling convnets with masked autoencoders. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 16133–16142 (2023).

Wang, J. et al. Deep high-resolution representation learning for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 43 , 3349–3364 (2020).

Chen, L-C. et al . Rethinking atrous convolution for semantic image segmentation. Preprint arXiv:1706.05587 (2017).

He, K. et al . Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition 770–778 (2016).

Guo, M. H. et al. Segnext: Rethinking convolutional attention design for semantic segmentation. Adv. Neural Inf. Process. Syst. 35 , 1140–1156 (2022).

Ma, X., Zhang, X., Pun, M.-O. & Liu, M. A multilevel multimodal fusion transformer for remote sensing semantic segmentation. IEEE Trans. Geosci. Remote Sens. 62 , 1–15. https://doi.org/10.1109/TGRS.2024.3373033 (2024).

Rottensteiner, F., Sohn, G., Jung, J., Gerke, M., Baillard, C., Benitez, S., Breitkopf, U. The ISPRS Benchmark on Urban Object Classification and 3D Building Reconstruction. In Proceedings of the ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Melbourne, Australia, 25 August–1 September , Vol. I-3, 293–298 (2012).

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Acknowledgements

We thank the reviewers and editors for their insightful comments.

This work was supported in part by the Key R&D Program of Ningxia Autonomous Region: Ecological environment monitoring and platform development of ecological barrier protection system for Helan Mountain (2022CMG02014); the Open Fund of Key Laboratory of Monitoring, Evaluation and Early Warning of Territorial Spatial Planning Implementation, Ministry of Natural Resources (No: LMEE-KF2023001); the Natural Science Foundation of Chong Qing (Nos.CSTB2022NSCQ-MSX1671);in part by the Construction Project of Chongqing Postgraduate Joint Training Base (JDLHPYJD2019004). (Corresponding author: Jianping Pan.)

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Zheng Luo, Jianping Pan, Yimeng Li, Chen Qi & Xunxun Wang

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Conceptualization, Z.L. and J.P.P.; methodology, Z.L.; validation, Z.L.; formal analysis Z.L. Y.M.L. X.X.W. and C.Q. ; resources, Y.H. and L.D. and J.P.P.; writing—original draft preparation, Z.L.; writ-ing—review and editing, Z.L and J.P.P.; All authors have read and agreed to the published version of the manuscript.

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Luo, Z., Pan, J., Hu, Y. et al. RS-Dseg: semantic segmentation of high-resolution remote sensing images based on a diffusion model component with unsupervised pretraining. Sci Rep 14 , 18609 (2024). https://doi.org/10.1038/s41598-024-69022-1

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Hermes 3 was meticulously trained using synthesized data and supervised fine-tuning on Meta’s Llama 3.1 405B base model. This was followed by reinforcement learning from human feedback (RLHF) and finally, quantization using Neural Magic’s FP8 method. This optimization effectively reduces the model's VRAM and disk requirements by approximately 50%, allowing it to run on a single node.

“Since the start of my journey in AI I wanted to bring about the realization of an open source frontier level model that aligns to you, the user - not some corporation or higher authority before the user. Today, with Hermes 3 405B, we've achieved that goal, a model that is frontier level, but truly aligned to you.  Thanks to our hard work on data synthesis and post training research, we were able to make a dataset that is fully synthetic over almost a year in the making to train Hermes 3 - and will be releasing much more to come.”

-Teknium, cofounder of Nous Research

For those seeking dedicated access and flexibility, Hermes 3 can run on a single node (available on-demand on Lambda’s Cloud ), or quickly scale to a multi-node 1-Click Cluster for further fine-tuning using Lambda's scalable cluster infrastructure. 

T ry Hermes 3 for free - for a limited time!

We’re excited to offer the AI/ML community free access to Hermes 3 through Lambda’s new Chat Completions API, fully compatible with the OpenAI API. It provides endpoints for creating completions, chat completions and listing models. No complex setup is required—simply generate a Cloud API key from Lambda’s dashboard ( sign-up ) and start exploring with our documentation ’s help.  For a more interactive experience, we’re also providing a simple chat interface: try your prompts in Lambda Chat !

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AI Tools 101

Flux by black forest labs: the next leap in text-to-image models. is it better than midjourney.

Table Of Contents

Black Forest Labs , the team behind the groundbreaking Stable Diffusion model, has released Flux – a suite of state-of-the-art models that promise to redefine the capabilities of AI-generated imagery. But does Flux truly represent a leap forward in the field, and how does it stack up against industry leaders like Midjourney? Let's dive deep into the world of Flux and explore its potential to reshape the future of AI-generated art and media.

The Birth of Black Forest Labs

Before we delve into the technical aspects of Flux, it's crucial to understand the pedigree behind this innovative model. Black Forest Labs is not just another AI startup; it's a powerhouse of talent with a track record of developing foundational generative AI models. The team includes the creators of VQGAN, Latent Diffusion, and the Stable Diffusion family of models that have taken the AI art world by storm.

Black Forest Labs Open-Source FLUX.1

With a successful Series Seed funding round of $31 million led by Andreessen Horowitz and support from notable angel investors, Black Forest Labs has positioned itself at the forefront of generative AI research. Their mission is clear: to develop and advance state-of-the-art generative deep learning models for media such as images and videos, while pushing the boundaries of creativity, efficiency, and diversity.

Introducing the Flux Model Family

Black Forest Labs has introduced the FLUX.1 suite of text-to-image models, designed to set new benchmarks in image detail, prompt adherence, style diversity, and scene complexity. The Flux family consists of three variants, each tailored to different use cases and accessibility levels:

• FLUX.1 [pro] : The flagship model, offering top-tier performance in image generation with superior prompt following, visual quality, image detail, and output diversity. Available through an API, it's positioned as the premium option for professional and enterprise use.
• FLUX.1 [dev] : An open-weight, guidance-distilled model for non-commercial applications. It's designed to achieve similar quality and prompt adherence capabilities as the pro version while being more efficient.
• FLUX.1 [schnell] : The fastest model in the suite, optimized for local development and personal use. It's openly available under an Apache 2.0 license, making it accessible for a wide range of applications and experiments.

I'll provide some unique and creative prompt examples that showcase FLUX.1's capabilities. These prompts will highlight the model's strengths in handling text, complex compositions, and challenging elements like hands.

• Artistic Style Blending with Text: “Create a portrait of Vincent van Gogh in his signature style, but replace his beard with swirling brush strokes that form the words ‘Starry Night' in cursive.”

• Dynamic Action Scene with Text Integration: “A superhero bursting through a comic book page. The action lines and sound effects should form the hero's name ‘FLUX FORCE' in bold, dynamic typography.”

• Surreal Concept with Precise Object Placement: “Close-up of a cute cat with brown and white colors under window sunlight. Sharp focus on eye texture and color. Natural lighting to capture authentic eye shine and depth.”

These prompts are designed to challenge FLUX.1's capabilities in text rendering, complex scene composition, and detailed object creation, while also showcasing its potential for creative and unique image generation.

Technical Innovations Behind Flux

At the heart of Flux's impressive capabilities lies a series of technical innovations that set it apart from its predecessors and contemporaries:

Transformer-powered Flow Models at Scale

All public FLUX.1 models are built on a hybrid architecture that combines multimodal and parallel diffusion transformer blocks, scaled to an impressive 12 billion parameters. This represents a significant leap in model size and complexity compared to many existing text-to-image models.

The Flux models improve upon previous state-of-the-art diffusion models by incorporating flow matching, a general and conceptually simple method for training generative models. Flow matching provides a more flexible framework for generative modeling, with diffusion models being a special case within this broader approach.

To enhance model performance and hardware efficiency, Black Forest Labs has integrated rotary positional embeddings and parallel attention layers. These techniques allow for better handling of spatial relationships in images and more efficient processing of large-scale data.

Architectural Innovations

Let's break down some of the key architectural elements that contribute to Flux's performance:

• Hybrid Architecture : By combining multimodal and parallel diffusion transformer blocks, Flux can effectively process both textual and visual information, leading to better alignment between prompts and generated images.
• Flow Matching : This approach allows for more flexible and efficient training of generative models. It provides a unified framework that encompasses diffusion models and other generative techniques, potentially leading to more robust and versatile image generation.
• Rotary Positional Embeddings : These embeddings help the model better understand and maintain spatial relationships within images, which is crucial for generating coherent and detailed visual content.
• Parallel Attention Layers : This technique allows for more efficient processing of attention mechanisms, which are critical for understanding relationships between different elements in both text prompts and generated images.
• Scaling to 12B Parameters : The sheer size of the model allows it to capture and synthesize more complex patterns and relationships, potentially leading to higher quality and more diverse outputs.

Benchmarking Flux: A New Standard in Image Synthesis

https://blackforestlabs.ai/announcing-black-forest-labs/

Black Forest Labs claims that FLUX.1 sets new standards in image synthesis, surpassing popular models like Midjourney v6.0, DALL·E 3 (HD), and SD3-Ultra in several key aspects:

• Visual Quality : Flux aims to produce images with higher fidelity, more realistic details, and better overall aesthetic appeal.
• Prompt Following : The model is designed to adhere more closely to the given text prompts, generating images that more accurately reflect the user's intentions.
• Size/Aspect Variability : Flux supports a diverse range of aspect ratios and resolutions, from 0.1 to 2.0 megapixels, offering flexibility for various use cases.
• Typography : The model shows improved capabilities in generating and rendering text within images, a common challenge for many text-to-image models.
• Output Diversity : Flux is specifically fine-tuned to preserve the entire output diversity from pretraining, offering a wider range of creative possibilities.

Flux vs. Midjourney: A Comparative Analysis

Now, let's address the burning question: Is Flux better than Midjourney ? To answer this, we need to consider several factors:

Image Quality and Aesthetics

Both Flux and Midjourney are known for producing high-quality, visually stunning images. Midjourney has been praised for its artistic flair and ability to create images with a distinct aesthetic appeal. Flux, with its advanced architecture and larger parameter count, aims to match or exceed this level of quality.

Early examples from Flux show impressive detail, realistic textures, and a strong grasp of lighting and composition. However, the subjective nature of art makes it difficult to definitively claim superiority in this area. Users may find that each model has its strengths in different styles or types of imagery.

Prompt Adherence

One area where Flux potentially edges out Midjourney is in prompt adherence. Black Forest Labs has emphasized their focus on improving the model's ability to accurately interpret and execute on given prompts. This could result in generated images that more closely match the user's intentions, especially for complex or nuanced requests.

Midjourney has sometimes been criticized for taking creative liberties with prompts, which can lead to beautiful but unexpected results. Flux's approach may offer more precise control over the generated output.

Speed and Efficiency

With the introduction of FLUX.1 [schnell], Black Forest Labs is targeting one of Midjourney's key advantages: speed. Midjourney is known for its rapid generation times, which has made it popular for iterative creative processes. If Flux can match or exceed this speed while maintaining quality, it could be a significant selling point.

Accessibility and Ease of Use

Midjourney has gained popularity partly due to its user-friendly interface and integration with Discord. Flux, being newer, may need time to develop similarly accessible interfaces. However, the open-source nature of FLUX.1 [schnell] and [dev] models could lead to a wide range of community-developed tools and integrations, potentially surpassing Midjourney in terms of flexibility and customization options.

Technical Capabilities

Flux's advanced architecture and larger model size suggest that it may have more raw capability in terms of understanding complex prompts and generating intricate details. The flow matching approach and hybrid architecture could allow Flux to handle a wider range of tasks and generate more diverse outputs.

Ethical Considerations and Bias Mitigation

Both Flux and Midjourney face the challenge of addressing ethical concerns in AI-generated imagery, such as bias, misinformation, and copyright issues. Black Forest Labs' emphasis on transparency and their commitment to making models widely accessible could potentially lead to more robust community oversight and faster improvements in these areas.

Code Implementation and Deployment

Using flux with diffusers.

Flux models can be easily integrated into existing workflows using the Hugging Face Diffusers library . Here's a step-by-step guide to using FLUX.1 [dev] or FLUX.1 [schnell] with Diffusers:

• First, install or upgrade the Diffusers library:
• Then, you can use the FluxPipeline to run the model:

This code snippet demonstrates how to load the FLUX.1 [dev] model, generate an image from a text prompt, and save the result.

Deploying Flux as an API with LitServe

For those looking to deploy Flux as a scalable API service, Black Forest Labs provides an example using LitServe, a high-performance inference engine. Here's a breakdown of the deployment process:

Define the model server:

This code sets up a LitServe API for Flux, including model loading, request handling, image generation, and response encoding.

Start the server:

Use the model api:.

You can test the API using a simple client script:

Key Features of the Deployment

• Serverless Architecture : The LitServe setup allows for scalable, serverless deployment that can scale to zero when not in use.
• Private API : You can deploy Flux as a private API on your own infrastructure.
• Multi-GPU Support : The setup is designed to work efficiently across multiple GPUs.
• Quantization : The code demonstrates how to quantize the model to 8-bit precision, allowing it to run on less powerful hardware like NVIDIA L4 GPUs.
• CPU Offloading : The enable_model_cpu_offload() method is used to conserve GPU memory by offloading parts of the model to CPU when not in use.

Practical Applications of Flux

The versatility and power of Flux open up a wide range of potential applications across various industries:

• Creative Industries : Graphic designers, illustrators, and artists can use Flux to quickly generate concept art, mood boards, and visual inspirations.
• Marketing and Advertising : Marketers can create custom visuals for campaigns, social media content, and product mockups with unprecedented speed and quality.
• Game Development : Game designers can use Flux to rapidly prototype environments, characters, and assets, streamlining the pre-production process.
• Architecture and Interior Design : Architects and designers can generate realistic visualizations of spaces and structures based on textual descriptions.
• Education : Educators can create custom visual aids and illustrations to enhance learning materials and make complex concepts more accessible.
• Film and Animation : Storyboard artists and animators can use Flux to quickly visualize scenes and characters, accelerating the pre-visualization process.

The Future of Flux and Text-to-Image Generation

Black Forest Labs has made it clear that Flux is just the beginning of their ambitions in the generative AI space. They've announced plans to develop competitive generative text-to-video systems, promising precise creation and editing capabilities at high definition and unprecedented speed.

This roadmap suggests that Flux is not just a standalone product but part of a broader ecosystem of generative AI tools. As the technology evolves, we can expect to see:

• Improved Integration : Seamless workflows between text-to-image and text-to-video generation, allowing for more complex and dynamic content creation.
• Enhanced Customization : More fine-grained control over generated content, possibly through advanced prompt engineering techniques or intuitive user interfaces.
• Real-time Generation : As models like FLUX.1 [schnell] continue to improve, we may see real-time image generation capabilities that could revolutionize live content creation and interactive media.
• Cross-modal Generation : The ability to generate and manipulate content across multiple modalities (text, image, video, audio) in a cohesive and integrated manner.
• Ethical AI Development : Continued focus on developing AI models that are not only powerful but also responsible and ethically sound.

Conclusion: Is Flux Better Than Midjourney?

The question of whether Flux is “better” than Midjourney is not easily answered with a simple yes or no. Both models represent the cutting edge of text-to-image generation technology, each with its own strengths and unique characteristics.

Flux, with its advanced architecture and emphasis on prompt adherence, may offer more precise control and potentially higher quality in certain scenarios. Its open-source variants also provide opportunities for customization and integration that could be highly valuable for developers and researchers.

Midjourney , on the other hand, has a proven track record, a large and active user base, and a distinctive artistic style that many users have come to love. Its integration with Discord and user-friendly interface have made it highly accessible to creatives of all technical skill levels.

Ultimately, the “better” model may depend on the specific use case, personal preferences, and the evolving capabilities of each platform. What's clear is that Flux represents a significant step forward in the field of generative AI, introducing innovative techniques and pushing the boundaries of what's possible in text-to-image synthesis.

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How AI models are getting smarter

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T ype in a question to Chat GPT and an answer will materialise. Put a prompt into DALL - E 3 and an image will emerge. Click on TikTok’s “for you” page and you will be fed videos to your taste. Ask Siri for the weather and in a moment it will be spoken back to you.

All these things are powered by artificial-intelligence ( AI ) models. Most rely on a neural network, trained on massive amounts of information—text, images and the like—relevant to how it will be used. Through much trial and error the weights of connections between simulated neurons are tuned on the basis of these data, akin to adjusting billions of dials until the output for a given input is satisfactory.

There are many ways to connect and layer neurons into a network. A series of advances in these architectures has helped researchers build neural networks which can learn more efficiently and which can extract more useful findings from existing datasets, driving much of the recent progress in AI .

Most of the current excitement has been focused on two families of models: large language models ( LLM s) for text, and diffusion models for images. These are deeper (ie, have more layers of neurons) than what came before, and are organised in ways that let them churn quickly through reams of data.

LLM s—such as GPT , Gemini, Claude and Llama—are all built on the so-called transformer architecture. Introduced in 2017 by Ashish Vaswani and his team at Google Brain, the key principle of transformers is that of “attention”. An attention layer allows a model to learn how multiple aspects of an input—such as words at certain distances from each other in text—are related to each other, and to take that into account as it formulates its output. Many attention layers in a row allow a model to learn associations at different levels of granularity—between words, phrases or even paragraphs. This approach is also well-suited for implementation on graphics-processing unit ( GPU ) chips, which has allowed these models to scale up and has, in turn, ramped up the market capitalisation of Nvidia, the world’s leading GPU -maker.

Transformer-based models can generate images as well as text. The first version of DALL - E , released by Open AI in 2021, was a transformer that learned associations between groups of pixels in an image, rather than words in a text. In both cases the neural network is translating what it “sees” into numbers and performing maths (specifically, matrix operations) on them. But transformers have their limitations. They struggle to learn consistent world-models. For example, when fielding a human’s queries they will contradict themselves from one answer to the next, without any “understanding” that the first answer makes the second nonsensical (or vice versa), because they do not really “know” either answer—just associations of certain strings of words that look like answers.

And as many now know, transformer-based models are prone to so-called “hallucinations” where they make up plausible-looking but wrong answers, and citations to support them. Similarly, the images produced by early transformer-based models often broke the rules of physics and were implausible in other ways (which may be a feature for some users, but was a bug for designers who sought to produce photo-realistic images). A different sort of model was needed.

Not my cup of tea

Enter diffusion models, which are capable of generating far more realistic images. The main idea for them was inspired by the physical process of diffusion. If you put a tea bag into a cup of hot water, the tea leaves start to steep and the colour of the tea seeps out, blurring into clear water. Leave it for a few minutes and the liquid in the cup will be a uniform colour. The laws of physics dictate this process of diffusion. Much as you can use the laws of physics to predict how the tea will diffuse, you can also reverse-engineer this process—to reconstruct where and how the tea bag might first have been dunked. In real life the second law of thermodynamics makes this a one-way street; one cannot get the original tea bag back from the cup. But learning to simulate that entropy-reversing return trip makes realistic image-generation possible.

Training works like this. You take an image and apply progressively more blur and noise, until it looks completely random. Then comes the hard part: reversing this process to recreate the original image, like recovering the tea bag from the tea. This is done using “self-supervised learning”, similar to how LLM s are trained on text: covering up words in a sentence and learning to predict the missing words through trial and error. In the case of images, the network learns how to remove increasing amounts of noise to reproduce the original image. As it works through billions of images, learning the patterns needed to remove distortions, the network gains the ability to create entirely new images out of nothing more than random noise.

Most state-of-the-art image-generation systems use a diffusion model, though they differ in how they go about “de-noising” or reversing distortions. Stable Diffusion (from Stability AI ) and Imagen, both released in 2022, used variations of an architecture called a convolutional neural network ( CNN ), which is good at analysing grid-like data such as rows and columns of pixels. CNN s, in effect, move small sliding windows up and down across their input looking for specific artefacts, such as patterns and corners. But though CNN s work well with pixels, some of the latest image-generators use so-called diffusion transformers, including Stability AI ’s newest model, Stable Diffusion 3. Once trained on diffusion, transformers are much better able to grasp how various pieces of an image or frame of video relate to each other, and how strongly or weakly they do so, resulting in more realistic outputs (though they still make mistakes).

Recommendation systems are another kettle of fish. It is rare to get a glimpse at the innards of one, because the companies that build and use recommendation algorithms are highly secretive about them. But in 2019 Meta, then Facebook, released details about its deep-learning recommendation model ( DLRM ). The model has three main parts. First, it converts inputs (such as a user’s age or “likes” on the platform, or content they consumed) into “embeddings”. It learns in such a way that similar things (like tennis and ping pong) are close to each other in this embedding space.

The DLRM then uses a neural network to do something called matrix factorisation. Imagine a spreadsheet where the columns are videos and the rows are different users. Each cell says how much each user likes each video. But most of the cells in the grid are empty. The goal of recommendation is to make predictions for all the empty cells. One way a DLRM might do this is to split the grid (in mathematical terms, factorise the matrix) into two grids: one that contains data about users, and one that contains data about the videos. By recombining these grids (or multiplying the matrices) and feeding the results into another neural network for more number-crunching, it is possible to fill in the grid cells that used to be empty—ie, predict how much each user will like each video.

The same approach can be applied to advertisements, songs on a streaming service, products on an e-commerce platform, and so forth. Tech firms are most interested in models that excel at commercially useful tasks like this. But running these models at scale requires extremely deep pockets, vast quantities of data and huge amounts of processing power.

Wait until you see next year’s model

In academic contexts, where datasets are smaller and budgets are constrained, other kinds of models are more practical. These include recurrent neural networks (for analysing sequences of data), variational autoencoders (for spotting patterns in data), generative adversarial networks (where one model learns to do a task by repeatedly trying to fool another model) and graph neural networks (for predicting the outcomes of complex interactions).

Just as deep neural networks, transformers and diffusion models all made the leap from research curiosities to widespread deployment, features and principles from these other models will be seized upon and incorporated into future AI models. Transformers are highly efficient, but it is not clear that scaling them up can solve their tendencies to hallucinate and to make logical errors when reasoning. The search is already under way for “post-transformer” architectures, from “state-space models” to “neuro-symbolic” AI , that can overcome such weaknesses and enable the next leap forward. Ideally such an architecture would combine attention with greater prowess at reasoning. Right now no human yet knows how to build that kind of model. Maybe someday an AI model will do the job.  ■

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Computer Science > Networking and Internet Architecture

Title: diffsg: a generative solver for network optimization with diffusion model.

Abstract: Diffusion generative models, famous for their performance in image generation, are popular in various cross-domain applications. However, their use in the communication community has been mostly limited to auxiliary tasks like data modeling and feature extraction. These models hold greater promise for fundamental problems in network optimization compared to traditional machine learning methods. Discriminative deep learning often falls short due to its single-step input-output mapping and lack of global awareness of the solution space, especially given the complexity of network optimization's objective functions. In contrast, diffusion generative models can consider a broader range of solutions and exhibit stronger generalization by learning parameters that describe the distribution of the underlying solution space, with higher probabilities assigned to better solutions. We propose a new framework Diffusion Model-based Solution Generation (DiffSG), which leverages the intrinsic distribution learning capabilities of diffusion generative models to learn high-quality solution distributions based on given inputs. The optimal solution within this distribution is highly probable, allowing it to be effectively reached through repeated sampling. We validate the performance of DiffSG on several typical network optimization problems, including mixed-integer non-linear programming, convex optimization, and hierarchical non-convex optimization. Our results show that DiffSG outperforms existing baselines. In summary, we demonstrate the potential of diffusion generative models in tackling complex network optimization problems and outline a promising path for their broader application in the communication community.

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Researchers from Apple have introduced a groundbreaking approach known as Matryoshka Diffusion Models (MDM) to address these challenges in high-resolution image and video generation. MDM stands out by integrating a hierarchical structure into the diffusion process, eliminating the need for separate stages that complicate training and inference in traditional models. This innovative method enables the generation of high-resolution content more efficiently and with greater scalability, marking a significant advancement in the field of AI-driven visual content creation.

The MDM methodology is built on a NestedUNet architecture, where the features and parameters for smaller-scale inputs are embedded within those of larger scales. This nesting allows the model to handle multiple resolutions simultaneously, significantly improving training speed and resource efficiency. The researchers also introduced a progressive training schedule that starts with low-resolution inputs and gradually increases the resolution as training progresses. This approach speeds up the training process and enhances the model’s ability to optimize for high-resolution outputs. The architecture’s hierarchical nature ensures that computational resources are allocated efficiently across different resolution levels, leading to more effective training and inference.

The performance of MDM is noteworthy, particularly in its ability to achieve high-quality results with less computational overhead compared to existing models. The research team from Apple demonstrated that MDM could train high-resolution models up to 1024×1024 pixels using the CC12M dataset, which contains 12 million images. Despite the relatively small size of the dataset, MDM achieved strong zero-shot generalization, meaning it performed well on new data without the need for extensive fine-tuning. The model’s efficiency is further highlighted by its ability to produce high-resolution images with Frechet Inception Distance (FID) scores that are competitive with state-of-the-art methods. For instance, MDM achieved a FID score of 6.62 on ImageNet 256×256 and 13.43 on MS-COCO 256×256, demonstrating its capability to generate high-quality images efficiently.

In conclusion, the introduction of Matryoshka Diffusion Models by researchers at Apple represents a significant step forward in high-resolution image and video generation. By leveraging a hierarchical structure and a progressive training schedule, MDM offers a more efficient and scalable solution than traditional methods. This advancement addresses the inefficiencies and complexities of existing diffusion models and paves the way for more practical and resource-efficient applications of AI-driven visual content creation. As a result, MDM holds great potential for future developments in the field, providing a robust framework for generating high-quality images and videos with reduced computational demands.

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Nikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.

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When games influence words: gaming addiction among college students increases verbal aggression through risk-biased drifting in decision-making.

1. Introduction

1.1. game addiction and game violence, 1.2. verbal aggression, 1.3. inhibitory control, 1.4. risk preference, 1.5. mediation model, 1.6. the present study, 2.1. participants, 2.2. research instruments, 2.2.1. questionnaires, 2.2.2. antisaccade task, 2.2.3. go/no-go task, 2.2.4. the cup task, 2.3. procedure, 2.4. statistical analysis, 2.4.1. validity analysis, 2.4.2. the hierarchical drift diffusion model, 2.4.3. mediation model, 3.1. correlational analysis and the cut-off point for gaming addiction, 3.2. validity analysis, 3.3. hierarchical drift diffusion model, 3.4. mediation model, 4. discussion, 4.1. mediation model, 4.2. risk preference, 4.3. inhibitory control, 4.4. contribution of the present study, 4.5. limitations, 5. conclusions, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

• Statista. Number of Video Gamers Worldwide in 2021, by Region. 2022. Available online: https://www.statista.com/statistics/293304/number-video-gamers/#:~:text=Figures%20in%202020%20showed%20that,across%20the%20globe%20in%202020 (accessed on 23 October 2023).
• Statista. Number of Video Gamers in the United States in 2022, by Engagement Level. 2022. Available online: https://www.statista.com/statistics/300942/number-core-gamers-usa/ (accessed on 23 October 2023).
• Bickham, D.S. Current Research and Viewpoints on Internet Addiction in Adolescents. Curr. Pediatr. Rep. 2021 , 9 , 1–10. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Chen, Y.-Y.; Yim, H.; Lee, T.-H. Negative Impact of Daily Screen Use on Inhibitory Control Network in Preadolescence: A Two-Year Follow-up Study. Dev. Cogn. Neurosci. 2023 , 60 , 101218. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Lin, H.-P.; Chen, K.-L.; Chou, W.; Yuan, K.-S.; Yen, S.-Y.; Chen, Y.-S.; Chow, J.C. Prolonged Touch Screen Device Usage Is Associated with Emotional and Behavioral Problems, but Not Language Delay, in Toddlers. Infant Behav. Dev. 2020 , 58 , 101424. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wiederhold, B.K. Kids Will Find a Way: The Benefits of Social Video Games. Cyberpsychology Behav. Soc. Netw. 2021 , 24 , 213–214. [ Google Scholar ] [ CrossRef ]
• Sokolov, A.A.; Collignon, A.; Bieler-Aeschlimann, M. Serious Video Games and Virtual Reality for Prevention and Neurorehabilitation of Cognitive Decline Because of Aging and Neurodegeneration. Curr. Opin. Neurol. 2020 , 33 , 239–248. [ Google Scholar ] [ CrossRef ]
• Anderson, C.A.; Bushman, B.J. Human Aggression. Annu. Rev. Psychol. 2001 , 52 , 27–51. [ Google Scholar ] [ CrossRef ]
• Buckley, K.E.; Anderson, C.A. A Theoretical Model of the Effects and Consequences of Playing Video Games. In Playing Video Games: Motives, Responses, and Consequences ; Vorderer, P., Bryant, J., Eds.; Lawrence Erlbaum Associates Publishers: Mahwah, NJ, USA, 2006; pp. 363–378. [ Google Scholar ]
• Drummond, A.; Sauer, J.D.; Ferguson, C.J. Do Longitudinal Studies Support Long-Term Relationships between Aggressive Game Play and Youth Aggressive Behaviour? A Meta-Analytic Examination. R. Soc. Open Sci. 2020 , 7 , 200373. [ Google Scholar ] [ CrossRef ]
• Mariano, T.E.; Gouveia, V.V.; Pimentel, C.E. The Effect of Video Games on Positive and Negative Cognitions. RPI 2020 , 12 , 7. [ Google Scholar ] [ CrossRef ]
• Shoshani, A.; Braverman, S.; Meirow, G. Video Games and Close Relations: Attachment and Empathy as Predictors of Children’s and Adolescents’ Video Game Social Play and Socio-Emotional Functioning. Comput. Hum. Behav. 2021 , 114 , 106578. [ Google Scholar ] [ CrossRef ]
• Tian, Y.; Gao, M.; Wang, P.; Gao, F. The Effects of Violent Video Games and Shyness on Individuals’ Aggressive Behaviors. Aggress. Behav. 2020 , 46 , 16–24. [ Google Scholar ] [ CrossRef ]
• Lee, E.-J.; Kim, H.S.; Choi, S. Violent Video Games and Aggression: Stimulation or Catharsis or Both? Cyberpsychology Behav. Soc. Netw. 2021 , 24 , 41–47. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Weber, R.; Behr, K.M.; Fisher, J.T.; Lonergan, C.; Quebral, C. Video Game Violence and Interactivity: Effect or Equivalence? J. Commun. 2020 , 70 , 219–244. [ Google Scholar ] [ CrossRef ]
• Li, S.; Wu, Z.; Zhang, Y.; Xu, M.; Wang, X.; Ma, X. Internet Gaming Disorder and Aggression: A Meta-Analysis of Teenagers and Young Adults. Front. Public Health 2023 , 11 , 1111889. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zhang, Q.; Cao, Y.; Tian, J. Effects of violent video games on players’ and observers’ aggressive cognitions and aggressive behaviors. J. Exp. Child Psychol. 2021 , 203 , 105005. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Khoo, S.S.; Yang, H. Smartphone Addiction and Checking Behaviors Predict Aggression: A Structural Equation Modeling Approach. IJERPH 2021 , 18 , 13020. [ Google Scholar ] [ CrossRef ]
• Addo, P.C.; Fang, J.; Kulbo, N.B.; Gumah, B.; Dagadu, J.C.; Li, L. Violent Video Games and Aggression Among Young Adults: The Moderating Effects of Adverse Environmental Factors. Cyberpsychology Behav. Soc. Netw. 2021 , 24 , 17–23. [ Google Scholar ] [ CrossRef ]
• Peng, C.; Guo, T.; Cheng, J.; Wang, M.; Rong, F.; Zhang, S.; Tan, Y.; Ding, H.; Wang, Y.; Yu, Y. Sex Differences in Association between Internet Addiction and Aggression among Adolescents Aged 12 to 18 in Mainland of China. J. Affect. Disord. 2022 , 312 , 198–207. [ Google Scholar ] [ CrossRef ]
• American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders , 5th ed.; American Psychiatric Publishing: Arlington, VA, USA, 2013. [ Google Scholar ] [ CrossRef ]
• Anderson, C.A.; Bushman, B.J.; Bartholow, B.D.; Cantor, J.; Christakis, D.; Coyne, S.M.; Donnerstein, E.; Brockmyer, J.F.; Gentile, D.A.; Green, C.S.; et al. Screen Violence and Youth Behavior. Pediatrics 2017 , 140 (Suppl. 2), S142–S147. [ Google Scholar ] [ CrossRef ]
• Király, O.; Sleczka, P.; Pontes, H.M.; Urbán, R.; Griffiths, M.D.; Demetrovics, Z. Validation of the Ten-Item Internet Gaming Disorder Test (IGDT-10) and evaluation of the nine DSM-5 Internet Gaming Disorder criteria. Addict. Behav. 2017 , 64 , 253–260. [ Google Scholar ] [ CrossRef ]
• Mohammad, S.; Jan, R.A.; Alsaedi, S.L. Symptoms, Mechanisms, and Treatments of Video Game Addiction. Cureus 2023 , 15 , e36957. [ Google Scholar ] [ CrossRef ]
• Mihara, S.; Higuchi, S. Cross-sectional and longitudinal epidemiological studies of Internet gaming disorder: A systematic review of the literature. Psychiatry Clin. Neurosci. 2017 , 71 , 425–444. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Adachi, P.J.; Willoughby, T. The Longitudinal Association between Competitive Video Game Play and Aggression among Adolescents and Young Adults. Child Dev. 2016 , 87 , 1877–1892. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wang, L.; Yang, G.; Zheng, Y.; Li, Z.; Qi, Y.; Li, Q.; Liu, X. Enhanced neural responses in specific phases of reward processing in individuals with Internet gaming disorder. J. Behav. Addict. 2021 , 10 , 99–111. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Fuji, K.; Yoshida, F. The influences of interaction during online gaming on sociability and aggression in real life. Shinrigaku Kenkyu Jpn. J. Psychol. 2010 , 80 , 494–503. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ducharme, P.; Kahn, J.; Vaudreuil, C.; Gusman, M.; Waber, D.; Ross, A.; Rotenberg, A.; Rober, A.; Kimball, K.; Peechatka, A.L.; et al. A “Proof of Concept” Randomized Controlled Trial of a Video Game Requiring Emotional Regulation to Augment Anger Control Training. Front. Psychiatry 2021 , 12 , 591906. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Chen, S.; Wei, M.; Wang, X.; Liao, J.; Li, J.; Liu, Y. Competitive Video Game Exposure Increases Aggression through Impulsivity in Chinese Adolescents: Evidence from a Multi-Method Study. J. Youth Adolesc. 2024; advance online publication . [ Google Scholar ] [ CrossRef ]
• Wei, L.; Zhang, S.; Turel, O.; Bechara, A.; He, Q. A Tripartite Neurocognitive Model of Internet Gaming Disorder. Front. Psychiatry 2017 , 8 , 285. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Gong, Y.; Li, Q.; Li, J.; Wang, X.; Jiang, W.; Zhao, W. Does Early Adversity Predict Aggression Among Chinese Male Violent Juvenile Offenders? The Mediating Role of Life History Strategy and the Moderating Role of Meaning in Life. BMC Psychol. 2023 , 11 , 382. [ Google Scholar ] [ CrossRef ]
• Glascock, J. Contribution of Verbally Aggressive TV Exposure and Perceived Reality to Trait Verbal Aggression. Commun. Rep. 2021 , 34 , 151–164. [ Google Scholar ] [ CrossRef ]
• McCarthy, B.; Boland, A.; Murphy, S.; Cooney, C. Vocally Disruptive Behavior: A Case Report and Literature Review. Ir. J. Psychol. Med. 2022 , 39 , 97–102. [ Google Scholar ] [ CrossRef ]
• Poling, D.V.; Smith, S.W. Perceptions about Verbal Aggression: Survey of Secondary Students with Emotional and Behavioral Disorders. J. Emot. Behav. Disord. 2023 , 31 , 14–26. [ Google Scholar ] [ CrossRef ]
• Infante, D.A.; Chandler, T.A.; Rudd, J.E. Test of an Argumentative Skill Deficiency Model of Interpersonal Violence. Commun. Monogr. 1989 , 56 , 163–177. [ Google Scholar ] [ CrossRef ]
• Joshi, A.; Sharma, K.; Sigdel, D.; Thapa, T.; Mehta, R. Internet Gaming Disorder and Aggression among Students on School Closure during COVID-19 Pandemic. J. Nepal Health Res. Counc. 2022 , 20 , 41–46. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Columb, D.; Wong, M.C.; O’Mahony, V.; Harrington, C.; Griffiths, M.D.; O’Gara, C. Gambling Advertising during Live Televised Male Sporting Events in Ireland: A Descriptive Study. Ir. J. Psychol. Med. 2023 , 40 , 134–142. [ Google Scholar ] [ CrossRef ]
• McNamee, P.; Mendolia, S.; Yerokhin, O. Social Media Use and Emotional and Behavioural Outcomes in Adolescence: Evidence from British Longitudinal Data. Econ. Hum. Biol. 2021 , 41 , 100992. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Parrish, K.H.; Smith, M.R.; Moran, L.; Ruberry, E.J.; Lengua, L.J. Tests of Bidirectional Relations of TV Exposure and Effortful Control as Predictors of Adjustment in Early Childhood in the Context of Family Risk Factors. Infant Child Dev. 2022 , 31 , e2314. [ Google Scholar ] [ CrossRef ]
• Limtrakul, N.; Louthrenoo, O.; Narkpongphun, A.; Boonchooduang, N.; Chonchaiya, W. Media Use and Psychosocial Adjustment in Children and Adolescents. J. Paediatr. Child Health 2018 , 54 , 296–301. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Varghese, N.E.; Santoro, E.; Lugo, A.; Madrid-Valero, J.J.; Ghislandi, S.; Torbica, A.; Gallus, S. The Role of Technology and Social Media Use in Sleep-Onset Difficulties among Italian Adolescents: Cross-sectional Study. J. Med. Internet Res. 2021 , 23 , e20319. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Brand, M.; Wegmann, E.; Stark, R.; Müller, A.; Wölfling, K.; Robbins, T.W.; Potenza, M.N. The Interaction of Person-Affect-Cognition-Execution (I-PACE) Model for Addictive Behaviors: Update, Generalization to Addictive Behaviors beyond Internet-Use Disorders, and Specification of the Process Character of Addictive Behaviors. Neurosci. Biobehav. Rev. 2019 , 104 , 1–10. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Argyriou, E.; Davison, C.B.; Lee, T.T.C. Response Inhibition and Internet Gaming Disorder: A Meta-analysis. Addict. Behav. 2017 , 71 , 54–60. [ Google Scholar ] [ CrossRef ]
• Bounoua, N.; Spielberg, J.M.; Sadeh, N. Clarifying the Synergistic Effects of Emotion Dysregulation and Inhibitory Control on Physical Aggression. Hum. Brain Mapp. 2022 , 43 , 5358–5369. [ Google Scholar ] [ CrossRef ]
• Dong, G.-H.; Potenza, M.N. Considering Gender Differences in the Study and Treatment of Internet Gaming Disorder. J. Psychiatr. Res. 2022 , 153 , 25–29. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Qiu, B.; Chen, Y.; He, X.; Liu, T.; Wang, S.; Zhang, W. Short-Term Touch-Screen Video Game Playing Improves the Inhibition Ability. Int. J. Environ. Res. Public Health 2021 , 18 , 6884. [ Google Scholar ] [ CrossRef ]
• Goldstein, R.; Volkow, N. Dysfunction of the prefrontal cortex in addiction: Neuroimaging findings and clinical implications. Nat. Rev. Neurosci. 2011 , 12 , 652–669. [ Google Scholar ] [ CrossRef ]
• Everitt, B.J.; Robbins, T.W. Drug Addiction: Updating Actions to Habits to Compulsions Ten Years On. Annu. Rev. Psychol. 2016 , 67 , 23–50. [ Google Scholar ] [ CrossRef ]
• Amlung, M.; Vedelago, L.; Acker, J.; Balodis, I.; MacKillop, J. Steep delay discounting and addictive behavior: A meta-analysis of continuous associations. Addiction 2017 , 112 , 51–62. [ Google Scholar ] [ CrossRef ]
• Turel, O.; He, Q.; Brevers, D.; Bechara, A. Delay discounting mediates the association between posterior insular cortex volume and social media addiction symptoms. Cogn. Affect. Behav. Neurosci. 2018 , 18 , 694–704. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Verdejo-Garcia, A.; Pérez-García, M.; Bechara, A. Emotion, decision-making and substance dependence: A somatic-marker model of addiction. Curr. Neuropharmacol. 2006 , 4 , 17–31. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Noh, D.; Shim, M.S. Factors influencing smartphone overdependence among adolescents. Sci. Rep. 2024 , 14 , 7725. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wang, H.; Xu, S.; Wang, S.; Wang, Y.; Chen, R. Using decision tree to predict non-suicidal self-injury among young adults: The role of depression, childhood maltreatment and recent bullying victimization. Eur. J. Psychotraumatol. 2024 , 15 , 2322390. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Dooren, M.M.M.V.; Visch, V.T.; Spijkerman, R. The Design and Application of Game Rewards in Youth Addiction Care. Information 2019 , 10 , 126. [ Google Scholar ] [ CrossRef ]
• Zheng, Y.; He, J.; Fan, L.; Qiu, Y. Reduction of Symptom after a Combined Behavioral Intervention for Reward Sensitivity and Rash Impulsiveness in Internet Gaming Disorder: A Comparative Study. J. Psychiatr. Res. 2022 , 153 , 159–166. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Dell’Osso, B.; Di Bernardo, I.; Vismara, M.; Piccoli, E.; Giorgetti, F.; Molteni, L.; Fineberg, N.A.; Virzì, C.; Bowden-Jones, H.; Truzoli, R.; et al. Managing Problematic Usage of the Internet and Related Disorders in an Era of Diagnostic Transition: An Updated Review. CPEMH 2021 , 17 , 61–74. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Müller, S.M.; Antons, S.; Wegmann, E.; Ioannidis, K.; King, D.L.; Potenza, M.N.; Chamberlain, S.R.; Brand, M. A Systematic Review and Meta-analysis of Risky Decision-making in Specific Domains of Problematic Use of the Internet: Evidence across Different Decision-making Tasks. Neurosci. Biobehav. Rev. 2023 , 152 , 105271. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Kim, Y.-J.; Lim, J.A.; Lee, J.Y.; Oh, S.; Kim, S.N.; Kim, D.J.; Ha, J.E.; Kwon, J.S.; Choi, J.-S. Impulsivity and Compulsivity in Internet Gaming Disorder: A Comparison with Obsessive–Compulsive Disorder and Alcohol Use Disorder. J. Behav. Addict. 2017 , 6 , 545–553. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Kräplin, A.; Scherbaum, S.; Kraft, E.-M.; Rehbein, F.; Bühringer, G.; Goschke, T.; Mößle, T. The Role of Inhibitory Control and Decision-making in the Course of Internet Gaming Disorder. J. Behav. Addict. 2021 , 9 , 990–1001. [ Google Scholar ] [ CrossRef ]
• Vega, A.; Cabello, R.; Megías-Robles, A.; Gómez-Leal, R.; Fernández-Berrocal, P. Emotional Intelligence and Aggressive Behaviors in Adolescents: A Systematic Review and Meta-Analysis. Trauma Violence Abus. 2022 , 23 , 1173–1183. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Yan, W.S.; Chen, R.T.; Liu, M.M.; Zheng, D.H. Monetary Reward Discounting, Inhibitory Control, and Trait Impulsivity in Young Adults with Internet Gaming Disorder and Nicotine Dependence. Front. Psychiatry 2021 , 12 , 628933. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Noël, X.; Brevers, D.; Bechara, A. A neurocognitive approach to understanding the neurobiology of addiction. Curr. Opin. Neurobiol. 2013 , 23 , 632–638. [ Google Scholar ] [ CrossRef ]
• Lin, Y.; Feng, T. Lateralization of self-control over the dorsolateral prefrontal cortex in decision-making: A systematic review and meta-analytic evidence from noninvasive brain stimulation. Cogn. Affect. Behav. Neurosci. 2024 , 24 , 19–41. [ Google Scholar ] [ CrossRef ]
• Laino Chiavegatti, G.; Floresco, S.B. Acute stress differentially alters reward-related decision making and inhibitory control under threat of punishment. Neurobiol. Stress 2024 , 30 , 100633. [ Google Scholar ] [ CrossRef ]
• Pontes, H.M.; Király, O.; Demetrovics, Z.; Griffiths, M.D. The Conceptualisation and Measurement of DSM-5 Internet Gaming Disorder: The Development of the IGD-20 Test. PLoS ONE 2014 , 9 , e110137. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Carlo, G.; Randall, B.A. The Development of a Measure of Prosocial Behaviors for Late Adolescents. J. Youth Adolesc. 2002 , 31 , 31–44. [ Google Scholar ] [ CrossRef ]
• Buss, A.H.; Perry, M. Personality Processes and Individual Differences. J. Pers. Soc. Psychol. 1992 , 63 , 452–459. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Miyake, A.; Friedman, N.P.; Emerson, M.J.; Witzki, A.H.; Howerter, A.; Wager, T.D. The Unity and Diversity of Executive Functions and Their Contributions to Complex “Frontal Lobe” Tasks: A Latent Variable Analysis. Cogn. Psychol. 2000 , 41 , 49–100. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Nosek, B.A.; Greenwald, A.G.; Banaji, M.R. Understanding and Using the Implicit Association Test: II. Method Variables and Construct Validity. Pers. Soc. Psychol. Bull. 2005 , 31 , 166–180. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Levin, I.P.; Hart, S.S. Risk Preferences in Young Children: Early Evidence of Individual Differences in Reaction to Potential Gains and Losses. J. Behav. Decis. Mak. 2003 , 16 , 397–413. [ Google Scholar ] [ CrossRef ]
• Gelman, A.; Rubin, D.B. Markov Chain Monte Carlo Methods in Biostatistics. Stat. Methods Med. Res. 1996 , 5 , 339–355. [ Google Scholar ] [ CrossRef ]
• Saleh, Y.; Jarratt-Barnham, I.; Petitet, P.; Fernandez-Egea, E.; Manohar, S.G.; Husain, M. Negative symptoms and cognitive impairment are associated with distinct motivational deficits in treatment resistant schizophrenia. Mol. Psychiatry 2023 , 28 , 4831–4841. [ Google Scholar ] [ CrossRef ]
• Jun, W. A Study on the Cause Analysis of Cyberbullying in Korean Adolescents. Int. J. Environ. Res. Public Health 2020 , 17 , 4648. [ Google Scholar ] [ CrossRef ]
• She, Y.; Yang, Z.; Xu, L.; Li, L. The Association between Violent Video Game Exposure and Sub-Types of School Bullying in Chinese Adolescents. Front. Psychiatry 2022 , 13 , 1026625. [ Google Scholar ] [ CrossRef ]
• Johnson, E.P.; Samp, J.A. Stoicism and Verbal Aggression in Serial Arguments: The Roles of Perceived Power, Perceived Resolvability, and Frequency of Arguments. J. Interpers. Violence 2022 , 37 , NP11836–NP11856. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Dong, G.; Huang, J.; Du, X. Enhanced reward sensitivity and decreased loss sensitivity in Internet addicts: An fMRI study during a guessing task. J. Psychiatr. Res. 2011 , 45 , 1525–1529. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Byrd, A.L.; Hawes, S.W.; Waller, R.; Delgado, M.R.; Sutherland, M.T.; Dick, A.S.; Trucco, E.M.; Riedel, M.C.; Pacheco-Colón, I.; Laird, A.R.; et al. Neural response to monetary loss among youth with disruptive behavior disorders and callous-unemotional traits in the ABCD study. NeuroImage Clin. 2021 , 32 , 102810. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wang, L.; Tian, M.; Zheng, Y.; Li, Q.; Liu, X. Reduced Loss Aversion and Inhibitory Control in Adolescents with Internet Gaming Disorder. Psychol. Addict. Behav. 2020 , 34 , 484–496. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Bediou, B.; Adams, D.M.; Mayer, R.E.; Tipton, E.; Green, C.S.; Bavelier, D. Meta-Analysis of Action Video Game Impact on Perceptual, Attentional, and Cognitive Skills. Psychol. Bull. 2018 , 144 , 77–110. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Pichon, S.; Bediou, B.; Antico, L.; Jack, R.; Garrod, O.; Sims, C.; Green, C.S.; Schyns, P.; Bavelier, D. Emotion perception in habitual players of action video games. Emotion 2021 , 21 , 1324–1339. [ Google Scholar ] [ CrossRef ]
• Foland-Ross, L.C.; Buckingam, B.; Mauras, N.; Arbelaez, A.M.; Tamborlane, W.V.; Tsalikian, E.; Cato, A.; Tong, G.; Englert, K.; Mazaika, P.K.; et al. Diabetes Research in Children Network (DirecNet). Executive task-based brain function in children with type 1 diabetes: An observational study. PLoS Med. 2019 , 16 , e1002979. [ Google Scholar ] [ CrossRef ]
• Lamp, G.; Sola Molina, R.M.; Hugrass, L.; Beaton, R.; Crewther, D.; Crewther, S.G. Kinematic Studies of the Go/No-Go Task as a Dynamic Sensorimotor Inhibition Task for Assessment of Motor and Executive Function in Stroke Patients: An Exploratory Study in a Neurotypical Sample. Brain Sci. 2022 , 12 , 1581. [ Google Scholar ] [ CrossRef ]
• Cabedo-Peris, J.; González-Sala, F.; Merino-Soto, C.; Pablo, J.Á.C.; Toledano-Toledano, F. Decision Making in Addictive Behaviors Based on Prospect Theory: A Systematic Review. Healthcare 2022 , 10 , 1659. [ Google Scholar ] [ CrossRef ]
• Schiebener, J.; Zamarian, L.; Delazer, M.; Brand, M. Executive functions, categorization of probabilities, and learning from feedback: What does really matter for decision making under explicit risk conditions? J. Clin. Exp. Neuropsychol. 2011 , 33 , 1025–1039. [ Google Scholar ] [ CrossRef ]
• Toplak, M.E.; Sorge, G.B.; Benoit, A.; West, R.F.; Stanovich, K.E. Decision-making and cognitive abilities: A review of associations between Iowa Gambling Task performance, executive functions, and intelligence. Clin. Psychol. Rev. 2010 , 30 , 562–581. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Stover, A.D.; Shulkin, J.; Lac, A.; Rapp, T. A meta-analysis of cognitive reappraisal and personal resilience. Clin. Psychol. Rev. 2024 , 110 , 102428. [ Google Scholar ] [ CrossRef ]
• Etxandi, M.; Baenas, I.; Mora-Maltas, B.; Granero, R.; Fernández-Aranda, F.; Tovar, S.; Solé-Morata, N.; Lucas, I.; Casado, S.; Gómez-Peña, M.; et al. Are Signals Regulating Energy Homeostasis Related to Neuropsychological and Clinical Features of Gambling Disorder? A Case-Control Study. Nutrients 2022 , 14 , 5084. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Riek, H.C.; Brien, D.C.; Coe, B.C.; Huang, J.; Perkins, J.E.; Yep, R.; McLaughlin, P.M.; Orange, J.B.; Peltsch, A.J.; Roberts, A.C.; et al. Cognitive correlates of antisaccade behaviour across multiple neurodegenerative diseases. Brain Commun. 2023 , 5 , fcad049. [ Google Scholar ] [ CrossRef ]
• Johansson, M.E.; Cameron, I.G.M.; Van der Kolk, N.M.; de Vries, N.M.; Klimars, E.; Toni, I.; Bloem, B.R.; Helmich, R.C. Aerobic Exercise Alters Brain Function and Structure in Parkinson’s Disease: A Randomized Controlled Trial. Ann. Neurol. 2022 , 91 , 203–216. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Magee, K.E.; McClaine, R.; Laurianti, V.; Connell, A.M. Effects of binge drinking and depression on cognitive-control processes during an emotional Go/No-Go task in emerging adults. J. Psychiatr. Res. 2023 , 162 , 161–169. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Reed, P. Impact of Social Media Use on Executive Function. Comput. Hum. Behav. 2023 , 141 , 107598. [ Google Scholar ] [ CrossRef ]
• Gao, Q.; Jia, G.; Zhao, J.; Zhang, D. Inhibitory Control in Excessive Social Networking Users: Evidence From an Event-Related Potential-Based Go-Nogo Task. Front. Psychol. 2019 , 10 , 1810. [ Google Scholar ] [ CrossRef ]
• Casey, B.J.; Jones, R.M.; Hare, T.A. The Adolescent Brain. Ann. N. Y. Acad. Sci. 2008 , 1124 , 111–126. [ Google Scholar ] [ CrossRef ]
• Galvan, A.; Hare, T.A.; Parra, C.E.; Penn, J.; Voss, H.; Glover, G.; Casey, B.J. Earlier Development of the Accumbens Relative to Orbitofrontal Cortex Might Underlie Risk-Taking Behavior in Adolescents. J. Neurosci. 2006 , 26 , 6885–6892. [ Google Scholar ] [ CrossRef ]
• Galvan, A.; Hare, T.; Voss, H.; Glover, G.; Casey, B. Risk-Taking and the Adolescent Brain: Who Is at Risk? Dev. Sci. 2007 , 10 , F8–F14. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Grace, A.A.; Floresco, S.B.; Goto, Y.; Lodge, D.J. Regulation of Firing of Dopaminergic Neurons and Control of Goal-Directed Behaviors. Trends Neurosci. 2007 , 30 , 220–227. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Larche, C.J.; Chini, K.; Lee, C.; Dixon, M.J. To Pay or Just Play? Examining Individual Differences Between Purchasers and Earners of Loot Boxes in Overwatch. J. Gambl. Stud. 2023 , 39 , 625–643. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zendle, D.; Flick, C.; Halgarth, D.; Ballou, N.; Cutting, J.; Drachen, A. The Relationship Between Lockdowns and Video Game Playtime: Multilevel Time-Series Analysis Using Massive-Scale Data Telemetry. J. Med. Internet Res. 2023 , 25 , e40190. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Lindenberg, K.; Holtmann, M. Einzug der Computerspielstörung als Verhaltenssucht in die ICD-11. Z. Kinder-Jugendpsychiatr. Psychother. 2022 , 50 , 1–7. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Lindenberg, K.; Kindt, S.; Szász-Janocha, C. Effectiveness of Cognitive Behavioral Therapy–Based Intervention in Preventing Gaming Disorder and Unspecified Internet Use Disorder in Adolescents: A Cluster Randomized Clinical Trial. JAMA Netw. Open 2022 , 5 , e2148995. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zack, M.; St. George, R.; Clark, L. Dopaminergic signaling of uncertainty and the aetiology of gambling addiction. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 2020 , 99 , 109853. [ Google Scholar ] [ CrossRef ]
• Dong, G.H.; Dai, J.; Potenza, M.N. Ten years of research on the treatments of internet gaming disorder: A scoping review and directions for future research. J. Behav. Addict. 2024 , 13 , 51–65. [ Google Scholar ] [ CrossRef ]
• Qin, L.; Liu, Q.; Luo, T. Reliability and Validity of the Chinese Version of the Internet Gaming Disorder Scale among College Students. Chin. J. Clin. Psychol. 2020 , 28 , 33–36. [ Google Scholar ] [ CrossRef ]
• Kou, Y.; Hong, H.; Tan, C.; Li, L. Revision of the Prosocial Tendency Scale for Adolescents. Psychol. Dev. Educ. 2007 , 1 , 112–117. [ Google Scholar ]
• Li, X.; Fei, L.; Zhang, Y.; Niu, Y.; Tong, Y.; Yang, S. Revision and Reliability and Validity of the Chinese Version of the Buss and Perry Aggression Questionnaire. Chin. J. Neurol. Psychiatr. 2011 , 10 , 607–613. [ Google Scholar ]

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Teng, H.; Zhu, L.; Zhang, X.; Qiu, B. When Games Influence Words: Gaming Addiction among College Students Increases Verbal Aggression through Risk-Biased Drifting in Decision-Making. Behav. Sci. 2024 , 14 , 699. https://doi.org/10.3390/bs14080699

Teng H, Zhu L, Zhang X, Qiu B. When Games Influence Words: Gaming Addiction among College Students Increases Verbal Aggression through Risk-Biased Drifting in Decision-Making. Behavioral Sciences . 2024; 14(8):699. https://doi.org/10.3390/bs14080699

Teng, Huina, Lixin Zhu, Xuanyu Zhang, and Boyu Qiu. 2024. "When Games Influence Words: Gaming Addiction among College Students Increases Verbal Aggression through Risk-Biased Drifting in Decision-Making" Behavioral Sciences 14, no. 8: 699. https://doi.org/10.3390/bs14080699

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