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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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DeepAR-Attention probabilistic prediction for stock price series
Stock price prediction is a significant research domain, intersecting statistics, finance, and economics. Accurately forecasting stock price trends...
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GVA: guided visual attention approach for automatic image caption generation
Automated image caption generation with attention mechanisms focuses on visual features including objects, attributes, actions, and scenes of the...
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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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DustNet: Attention to Dust
Detecting airborne dust in common RGB images is hard. Nevertheless, monitoring airborne dust can greatly contribute to climate protection,...
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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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Semantic-guided spatio-temporal attention for few-shot action recognition
Few-shot action recognition is a challenging problem aimed at learning a model capable of adapting to recognize new categories using only a few...
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Non-local self-attention network for image super-resolution
The utilization of self-attention mechanisms in Transformer-based methods has shown great potential in addressing the image super-resolution (SR)...
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A multi-attention Uformer for low-dose CT image denoising
Kee** the number of projection views constant and reducing the radiation dose at each view is an effective way to achieve low-dose CT. This will...
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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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Knowledge-enhanced personalized hierarchical attention network for sequential recommendation
Sequential recommendation aims to predict the next items that users will interact with according to the sequential dependencies within historical...
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Multi-level self attention for unsupervised learning person re-identification
In recent years, the task of person re-identification (ReID) has placed a critical demand on accurately describing image features. Attention...
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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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Research on person re-identification based on multi-level attention model
Person Re-identification (ReID) is an important research direction in the field of pattern recognition, which aims to retrieve the same pedestrian in...
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Multimodal attention-based transformer for video captioning
Video captioning is a computer vision task that generates a natural language description for a video. In this paper, we propose a multimodal...
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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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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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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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Sarcasm detection based on BERT and attention mechanism
Sarcasm detection is a challenging task in sentiment analysis and is usually used to detect sarcasm by judging inconsistencies in the individual...
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Residual shuffle attention network for image super-resolution
The image super-resolution reconstruction methods based on deep learning achieve satisfactory visual quality; however, the majority are difficult to...
