論文紹介：Is Space-Time Attention All You Need for Video Understanding?
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Gedas Bertasius, Heng Wang, Lorenzo Torresani, "Is Space-Time Attention All You Need for Video Understanding?" ICML2021 https://proceedings.mlr.press/v139/bertasius21a.html
![◼Transformer : TimeSformer
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• Self-Attention
◼Vision Transformer (ViT) [Dosovitskiy+, ICLR 2021]
• Transformer
• Embeding
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• Transformer Encoder
• Self-Attention
• MLP
• Head](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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論文紹介：Is Space-Time Attention All You Need for Video Understanding?
- 1. Is Space-Time Attention All You Need for Video Understanding? Gedas Bertasius, Heng Wang, Lorenzo Torresani, ICML2021 2023/5/11
- 2. ◼Transformer : TimeSformer • • Self-Attention ◼Vision Transformer (ViT) [Dosovitskiy+, ICLR 2021] • Transformer • Embeding • • Transformer Encoder • Self-Attention • MLP • Head
- 3. ◼ViViT [Arnab+, ICCV2021] • Embedding 3D Conv ◼ (TimeSformer) • ViT 2D Conv Embedding: 3D Conv Embedding: 2D Conv Transformer Encoder Transformer Encoder . . . . . . . . . . .. . .. Attention Self-Attention
- 4. ◼TimeSformer • • 2D Conv • Time Attention, Space Attention • Attention • Embedding: 2D Conv . . . . . . . .. Transformer Encoder Time Attention Space Attention × 12 Time Attn, Space Attn Time Attn Space Attn . . . +
- 5. Self-Attention Architectures ◼ Self-Attention • Space Attention (S) • Attn • Joint Space-Time Attention (ST) • Attn • Divided Space-Time Attention (S+T) • Attn • Sparse Local Global Attention (L+G) • Attn • Axial Attention (T+W+H) • Attn
- 6. ◼ • Kinetics-400 (K400) [Kay+, arXiv2017] • Kinetics-600 (K600) [Carreira+, arXiv2018] • Something-Something-v2 (SSv2) [Goyal+, ICCV2017] • Diving-48 [Li+, ECCV2018] ◼ • 224 × 224 • 8 • 1 32 ◼ • TimeSformer • TimeSformer-HR ◼ • ImageNet-21k (I21K) • ImageNet-1k (I1K) ◼ • 15 • Optimizer SGD • Momentum 0.9 • Weight decay 0.0001
- 7. 1. Analysis of Self-Attention Schemes 2. Comparison to 3D CNNs 3. Varying the Number of Tokens 4. The Importance of Positional Embeddings 5. Comparison to the State-of-the-Art
- 8. 1. Analysis of Self-Attention Schemes ✓Self-Attention • Space Attention (S) • Joint Space-Time Attention (ST) • Divided Space-Time Attention (S+T) • Sparse Local Global Attention (L+G) • Axial Attention (T+W+H) ✓ST S+T • 224, 336, 448, 560 • 8, 32, 64, 96 ◼ • K400, SSv2 • I21K
- 9. ◼Self-Attention • Divided Space-Time • Space Time Attention ◼ST S+T • S+T (Divided)
- 10. 2. Comparison to 3D CNNs ✓3D CNN • • • • • • I21K, I1K ◼ • TimeSformer • I3D R50 [Wang+, CVPR2018] • SlowFast R50 [Feichtenhofer+, ICCV2019] ◼ • K400 ✓ • I21K I1K ◼ • TimeSformer • 8 224 224 • TimeSformer-HR • 16 448 448 • TimeSformer-L • 96 224 224 ◼ • K400, SSv2
- 11. ◼3D CNN • TimeSformer • TimeSformer • I21K ◼ • TimeSformer I21K
- 12. 3. Varying the Number of Tokens ✓ • 224 (default), 336, 448, 560 • 8 (default), 32, 64, 96 ◼ • 16 × 16 224 336 448 560 8 8 × 14 × 14 8 × 21 × 21 8 × 28 × 28 8 × 35 × 35 32 32 × 14 × 14 32 × 21 × 21 32 × 28 × 28 32 × 35 × 35 64 64 × 14 × 14 64 × 21 × 21 64 × 28 × 28 64 × 35 × 35 96 96 × 14 × 14 96 × 21 × 21 96 × 28 × 28 96 × 35 × 35
- 13. ◼ • • ◼ •
- 14. The Importance of Positional Embeddings ◼ • • • • ◼ • K400, SSv2 • I21K Embedding: 2D Conv . . . . . . . .. Transformer Encoder Time Attention Space Attention . . . +
- 15. ◼Space-Time • up
- 16. Comparison to the State-of-the-Art ✓SOTA • R(2+1)D [Tran+, arXiv2018] • bLVNet [Fan+, 2019] • TSM [Lin+, ICCV2019] • S3D-G [Xie+, ECCV2018] • Oct-I3D+NL [Chen+, ICCV2019] • D3D [Stroud+, WACV2020] • I3D+NL [Wang+, CVPR2018] • Ip-CSN-152 [Tran+, ICCV2019] • CorrNet [Wang+, CVPR2020] • LGD-3D-101 [Qiu+, CVPR2019] • SlowFast [Feichtenhofer+, ICCV2019] • X3D-XXL [Feichtenhofer+, CVPR2020] ◼ • 1. K400, K600 2. SSv2, Div48 • • I21K ◼ • Top1, top5, TFLOPs
- 18. ◼Transformer : TimeSformer • • Self-Attention • Divided Space-Time Attention ◼ • • SOTA • ◼ • Self-Attention • 3D CNN • Token • Positional embedding