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BOAT: Bilateral Local Attention Vision Transformer. (arXiv:2201.13027v2 [cs.CV] UPDATED)
Oct. 20, 2022, 1:12 a.m. | Tan Yu, Gangming Zhao, Ping Li, Yizhou Yu
cs.LG updates on arXiv.org arxiv.org
Vision Transformers achieved outstanding performance in many computer vision
tasks. Early Vision Transformers such as ViT and DeiT adopt global
self-attention, which is computationally expensive when the number of patches
is large. To improve efficiency, recent Vision Transformers adopt local
self-attention mechanisms, where self-attention is computed within local
windows. Despite the fact that window-based local self-attention significantly
boosts efficiency, it fails to capture the relationships between distant but
similar patches in the image plane. To overcome this limitation of image-space …
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