Pruning Self-Attentions Into Convolutional Layers in Single Path
Vision Transformers (ViTs) have achieved impressive performance over various computer vision tasks. However, modeling global correlations with multi-head self-attention (MSA) layers leads to two widely recognized issues: the massive computational resource consumption and the lack of intrinsic induct...
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Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 46(2024), 5 vom: 05. Mai, Seite 3910-3922
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| 1. Verfasser: |
He, Haoyu
(VerfasserIn) |
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| Weitere Verfasser: |
Cai, Jianfei,
Liu, Jing,
Pan, Zizheng,
Zhang, Jing,
Tao, Dacheng,
Zhuang, Bohan |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2024
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence
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| Schlagworte: | Journal Article |
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