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KVT: k-NN Attention for Boosting Vision Transformers. (arXiv:2106.00515v2 [cs.CV] UPDATED)
Jan. 13, 2022, 2:10 a.m. | Pichao Wang, Xue Wang, Fan Wang, Ming Lin, Shuning Chang, Hao Li, Rong Jin
cs.CV updates on arXiv.org arxiv.org
Convolutional Neural Networks (CNNs) have dominated computer vision for
years, due to its ability in capturing locality and translation invariance.
Recently, many vision transformer architectures have been proposed and they
show promising performance. A key component in vision transformers is the
fully-connected self-attention which is more powerful than CNNs in modelling
long range dependencies. However, since the current dense self-attention uses
all image patches (tokens) to compute attention matrix, it may neglect locality
of images patches and involve noisy tokens …
More from arxiv.org / cs.CV updates on arXiv.org
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