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Pyramid Self-attention for Semantic Segmentation
2021Self-attention is vital in computer vision since it is the building block of Transformer and can model long-range context for visual recognition. However, computing pairwise self-attention between all pixels for dense prediction tasks (e.g., semantic segmentation) costs high computation.
Jiyang Qi +4 more
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Improve Image Captioning by Self-attention
2019The common attention mechanism has been widely adopted in prevalent image captioning frameworks. In most of the prior work, attention weights were only determined by visual features as well as the hidden states of Recurrent Neural Network (RNN), while the interaction of visual features was not modelled.
Zhenru Li, Yaoyi Li, Hongtao Lu 0001
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Beyond Self-Attention: External Attention Using Two Linear Layers for Visual Tasks
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022Zheng-Ning Liu +2 more
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Cascaded feature fusion with multi-level self-attention mechanism for object detection
Pattern Recognition, 2023Chuanxu Wang
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Self-attention neural architecture search for semantic image segmentation
Knowledge-Based Systems, 2022Guosheng Hu, Gai-Ge Wang, Junyu Dong
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Relation-mining self-attention network for skeleton-based human action recognition
Pattern Recognition, 2023Kumie Gedamu, Yanli Ji, Heng Tao Shen
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Relation constraint self-attention for image captioning
Neurocomputing, 2022Junzhong Ji +2 more
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MHSA-Net: Multihead Self-Attention Network for Occluded Person Re-Identification
IEEE Transactions on Neural Networks and Learning Systems, 2023Hongchen Tan, Xiuping Liu, Baocai Yin
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Multi-view self-attention networks
Knowledge-Based Systems, 2022Mingzhou Xu +3 more
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