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Image color rendering based on frequency channel attention GAN
In recent years, channel attention mechanism has greatly improved the performance of computer vision-oriented network models. But the simple...
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Leaf disease recognition based on channel information attention network
Aiming at the problem of the variety of plant leaf diseases and how to extract effective features, an attention network model fused with channel...
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Human-Object Interaction Detection with Channel Aware Attention
Human-object interaction detection (HOI) is a fundamental task in computer vision, which requires locating instances and predicting their...
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Adaptive attention mechanism for single channel speech enhancement
The recent development of speech enhancement methods has incorporated attention mechanisms for learning long-term speech signal dependencies. The...
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Single Image Deraining Using Residual Channel Attention Networks
Image deraining is a highly ill-posed problem. Although significant progress has been made due to the use of deep convolutional neural networks, this...
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An efficient multi-scale channel attention network for person re-identification
At present, occlusion and similar appearance pose serious challenges to the task of person re-identification. In this work, we propose an efficient...
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Combining channel-wise joint attention and temporal attention in graph convolutional networks for skeleton-based action recognition
Graph convolutional networks (GCNs) have been shown to be effective in performing skeleton-based action recognition, as graph topology has advantages...
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Fake news detection based on dual-channel graph convolutional attention network
Fake news detection has attracted significant attention since the spread of fake news on social media has affected the media’s credibility. Some...
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Image denoising using channel attention residual enhanced Swin Transformer
Transformers have achieved remarkable results in high-level vision tasks, but their application in low-level computer vision tasks such as image...
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Wide Activation Fourier Channel Attention Network for Super-Resolution
Attention mechanisms, especially channel attention, have been widely used in a wide range of tasks in computer vision. More recently, researchers...
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High-frequency channel attention and contrastive learning for image super-resolution
Over the last decade, convolutional neural networks (CNNs) have allowed remarkable advances in single image super-resolution (SISR). In general,...
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Deep recurrent residual channel attention network for single image super-resolution
The models based on convolutional neural network have achieved excellent results in image super-resolution by acquiring prior knowledge from a large...
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MSCA-UNet: multi-scale channel attention-based UNet for segmentation of medical ultrasound images
Since deep learning is introduced to medical image segmentation, UNet and its variants have been extensively applied in medical image analysis. This...
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Beyond coordinate attention: spatial-temporal recalibration and channel scaling for skeleton-based action recognition
Skeleton-based action recognition is an attractive issue in computer vision. Recent lightweight attention mechanisms (e.g. coordinate attention) have...
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MVT-CEAM: a lightweight MobileViT with channel expansion and attention mechanism for facial expression recognition
Facial expression recognition is a crucial area of study in psychology that can be applied to many fields, such as intelligent healthcare,...
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Light field angular super resolution based on residual channel attention and classification up-sampling
Current light field angular super resolution algorithms generate coarse viewpoint images due to their low learning ability and equally upsample all...
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Masked cross-attention and multi-head channel attention guiding single-stage generative adversarial networks for text-to-image generation
Although the text-to-image model aims to generate realistic images that correspond to the text description, generating high-quality, and accurate...
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Learning discriminative features for person re-identification via multi-spectral channel attention
Person re-identification (Re-ID) aims to match a particular person captured by different cameras, which has great potential in video surveillance....
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Joint 2D attention gate and channel-spatial attention network for retinal vessel segmentation of OCT-angiography images
OCT-angiography is a non-invasive visualization imaging technology with high resolution that can more clearly image tiny blood vessels. Certain...
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MC-GAT: Multi-Channel Graph Attention Networks for Capturing Diverse Information in Complex Graphs
Graph attention networks (GAT), which have strong performance in tackling various analytical tasks on network data, have attracted wide attention....
