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Image Deblurring Using Feedback Mechanism and Dual Gated Attention Network
Recently, image deblurring task driven by the encoder-decoder network has made a tremendous amount of progress. However, these encoder-decoder-based...
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Enhanced Hybrid Intrusion Detection System with Attention Mechanism using Deep Learning
The introduction of the Attention mechanism by the Internet of Things—or WSN-IoT—in the sector has greatly enhanced the intrusion detection mechanism...
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Transformer model incorporating local graph semantic attention for image caption
Aiming at the problem of isolating semantic information of existing transformer-based models in the image captioning tasks, a transformer model...
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Word-Context Attention for Text Representation
We tackle the insufficient context pattern limitation of existing Word-Word Attention caused by its spatial-shared property. To this end, we propose...
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Weakly supervised human skin segmentation using guidance attention mechanisms
Human skin segmentation is a crucial task in computer vision and biometric systems, yet it poses several challenges such as variability in skin...
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SSANet: spatial stain attention network for pathological images classification
Histopathological images classification plays a significant role in cancer diagnosis, but current deep learning methods fail to account for the...
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Multi-Modal Co-Attention Capsule Network for Fake News Detection
AbstractMost of the existing fake news identification models mainly focused on exploiting multi-modal features to enhanced performance recently. This...
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Focused or divided? Collaborative attention and technological orientations in technology transfer
Research and development (R&D) collaborations are assumed to be important means by which research institute facilitate technology transfers. However,...
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GRAN: ghost residual attention network for single image super resolution
Recently, many works have designed wider and deeper networks to achieve higher image super-resolution performance. Despite their outstanding...
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Attention U-Net for Kidney and Masses
We proposed new attention method using attention- unet architectures that reflects the 3d axial information to learn the spatial features of 3D...
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Cascading spatio-temporal attention network for real-time action detection
Accurately detecting human actions in video has many applications, such as video surveillance and somatosensory games. In this paper, we propose a...
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Cycle-attention-derain: unsupervised rain removal with CycleGAN
Single image deraining is a fundamental task in computer vision, which can greatly improve the performance of subsequent high-level tasks under rainy...
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Edge-guided generative network with attention for point cloud completion
Point clouds acquired through 3D scanning devices often suffer from sparsity and incompleteness due to reflection, device resolution, and viewing...
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GCAT: graph calibration attention transformer for robust object tracking
Recent Siamese trackers have taken advantage of transformers to achieve impressive advancements. However, existing transformer trackers ignore...
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Transformer-based multi-level attention integration network for video saliency prediction
Most existing models for video saliency prediction heavily rely on 3D convolutional operations to extract spatio-temporal features. However, it is...
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Position-category-aware attention network for next-item recommendation
The next-item recommendation can extract critical information from the historical sequence and predict the next actions of users. To better extract...
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MutualFormer: Multi-modal Representation Learning via Cross-Diffusion Attention
Aggregating multi-modal data to obtain reliable data representation attracts more and more attention. Recent studies demonstrate that Transformer...
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Masked-attention diffusion guidance for spatially controlling text-to-image generation
Text-to-image synthesis has achieved high-quality results with recent advances in diffusion models. However, text input alone has high spatial...
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Attribute- and attention-guided few-shot classification
The field of image classification faces significant challenges due to the scarcity of target samples, leading to model overfitting and difficult...
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Group attention retention network for co-salient object detection
The co-salient object detection (Co-SOD) aims to discover common, salient objects from a group of images. With the development of convolutional...
