How Does Attention Work in Vision Transformers? A Visual Analytics Attempt
Vision transformer (ViT) expands the success of transformer models from sequential data to images. The model decomposes an image into many smaller patches and arranges them into a sequence. Multi-head self-attentions are then applied to the sequence to learn the attention between patches. Despite ma...
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Détails bibliographiques
| Publié dans: | IEEE transactions on visualization and computer graphics. - 1996. - 29(2023), 6 vom: 05. Juni, Seite 2888-2900
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| Auteur principal: |
Li, Yiran
(Auteur) |
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| Autres auteurs: |
Wang, Junpeng,
Dai, Xin,
Wang, Liang,
Yeh, Chin-Chia Michael,
Zheng, Yan,
Zhang, Wei,
Ma, Kwan-Liu |
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| Format: | Article en ligne
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| Langue: | English |
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| Publié: |
2023
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| Accès à la collection: | IEEE transactions on visualization and computer graphics
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| Sujets: | Journal Article |
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