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CANet: Channel Extending and Axial Attention Catching Network for Multi-structure Kidney Segmentation
Renal cancer is one of the most prevalent cancers worldwide. Clinical signs of kidney cancer include hematuria and low back discomfort, which are...
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Visual attention network
While originally designed for natural language processing tasks, the self-attention mechanism has recently taken various computer vision areas by...
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One-Staged Attention-Based Neoplasms Recognition Method for Single-Channel Monochrome Computer Tomography Snapshots
AbstractComputer tomography is most commonly used for diagnosing lung cancer, which is one of the deadliest cancers in the world. Online services...
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Dual Siamese Channel Attention Networks for Visual Object Tracking
Siamese network based trackers have achieved remarkable performance on visual object tracking. The target position is determined by the similarity...
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MadFormer: multi-attention-driven image super-resolution method based on Transformer
While the Transformer-based method has demonstrated exceptional performance in low-level visual processing tasks, it has a strong modeling ability...
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Dual-branch and triple-attention network for pan-sharpening
Pan-sharpening is a technique used to generate high-resolution multi-spectral (HRMS) images by merging high-resolution panchromatic (PAN) images with...
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Esophageal tissue segmentation on OCT images with hybrid attention network
The accurate segmentation of the tissue layers of Optical Coherence Tomography (OCT) esophageal images has vital guiding significance for esophageal...
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One-Stage Classifiers Based on U-Net and Autoencoder with Attention for Recognition of Neoplasms from Single-Channel Monochrome Computed Tomography Images
AbstractCurrently, one of the most useful methods for diagnosing lung cancer is based on computed tomography. In addition, online medical...
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Evota: an enhanced visual object tracking network with attention mechanism
Transformer architecture has made breakthrough in various downstream computer vision tasks and has shown its great potential in visual object...
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Exploiting Item Relationships with Dual-Channel Attention Networks for Session-Based Recommendation
Session-based recommendation (SBR) is the task of recommending the next item for users based on their short-term behavior sequences. Most of the...
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Multi-Keys Attention Network for Image Captioning
The image captioning task aims to generate descriptions from the main content of images. Recently, the Transformer with a self-attention mechanism...
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EEG-based emotion recognition via capsule network with channel-wise attention and LSTM models
Emotion is a kind of psychological and physical state that people produce to objective things. Accurate recognition of emotions is very important in...
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Multi-feature self-attention super-resolution network
In recent years, single-image super-resolution (SISR) methods based on the attention mechanism have been widely explored and achieved remarkable...
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Boosting power line inspection in bad weather: Removing weather noise with channel-spatial attention-based UNet
Power line inspection based on UAVs can effectively improve the inspection efficiency. With the development of object detection algorithms, automatic...
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Face Morphing Detection Based on a Two-Stream Network with Channel Attention and Residual of Multiple Color Spaces
Aiming at the performance improvement of face morphing detection, a novel method is proposed by using a two-stream network with channel attention and...
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Abnormal event detection in surveillance videos based on multi-scale feature and channel-wise attention mechanism
Abnormal event detection is a challenging task, due to object scale variation, impact of background and anomaly defined differently in different...
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A topic-based multi-channel attention model under hybrid mode for image caption
Automatically generating captions of an image is not closely related to every spatial area of the visual information, but always related to the topic...
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Modified dual attention triplet-supervised hashing network for image retrieval
In view of the problems of insufficient feature extraction and ineffective capture of correlation between deep features in existing image retrieval...
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Multi-channel Orthogonal Decomposition Attention Network for Sequential Recommendation
Sequential recommender systems aim to model users’ evolving interests from historical behaviors and make customized recommendations. Except for...
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Lightweight dynamic attention network for single thermal image super-resolution
The embedding of attention mechanism in convolutional neural networks (CNN) effectively improves the performance of single image...
