論文紹介：STMixer: A One-Stage Sparse Action Detector

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Tao Wu, Mengqi Cao, Ziteng Gao, Gangshan Wu, Limin Wang, "STMixer: A One-Stage Sparse Action Detector" CVPR2023 https://openaccess.thecvf.com/content/CVPR2023/html/Wu_STMixer_A_One-Stage_Sparse_Action_Detector_CVPR_2023_paper.html

Technology

STMixer: A One-Stage Sparse
Action Detector
Tao Wu, Mengqi Cao, Ziteng Gao, Gangshan Wu, Limin Wang
CVPR2023
2023/9/27

◼
•
◼
• [Carion+, ECCV2020] End-to-End
•
•

◼
◼
◼
• box 𝑥𝑛, 𝑦𝑛, 𝑧𝑛, 𝑟𝑛
• 𝑥𝑛, 𝑦𝑛
• Box
• 𝑧𝑛
• box
• 𝑟𝑛
•

ASAM module
◼ 𝑄𝑠 𝑄𝑡 self-attention
•
◼
• Adaptive Spatiotemporal Feature Sampling
• Adaptive Dual-branch Feature Mixing
◼ 𝑄𝑝
′
,𝑄𝑠
′
,𝑄𝑡
′
ASAM module

Adaptive Spatiotemporal Feature Sampling
◼𝑄𝑠
𝑛
box
• 𝑃𝑖𝑛
◼
• 𝑇 × 𝑃𝑖𝑛
◼4
•
F𝑛 ∈ 𝑅𝑇×𝑃𝑖𝑛×𝐷

Adaptive Dual-branch Feature Mixing
◼
•
•
•
• 𝑀𝑐

Adaptive Dual-branch Feature Mixing
◼ 𝑀𝑝

◼Human score
• box
•
◼Action score
•
•
◼Human boxes
• Bounding box
•

◼
• AVA [Gu+, CVP2018]
◼
• D
• 256
• N
• 100
◼
• 𝜆𝑐𝑙𝑠 = 2.0, 𝜆𝐿1= 2.0
• 𝜆𝑔𝑖𝑜𝑢 = 2.0, 𝜆𝑎𝑐𝑡 = 24.0
◼Optimizer
• AdamW
•
• 1e-4
◼
• 16
◼
• 10
◼
• Mean Average Precision(mAP)
• IoU 0.5

Ablation Study
◼
• RoIAlign [He+, ICCV2017]
• box
•
•
◼
• 4
•

Ablation Study
◼ ASAM module
◼
•
•
• 32
•
• 100
• ASAM module
• 6

JHMDB UCF101-24
◼JHMDB [Jhuang+, ICCV2013] UCF101-24 [Singh+, ICCV2017]
◼
• JHMDB
• UCF101-24

◼End-to-End STMixer
◼AVA,JHMDB,UCF101-24
◼
•

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論文紹介：STMixer: A One-Stage Sparse Action Detector

1. STMixer: A One-Stage Sparse Action Detector Tao Wu, Mengqi Cao, Ziteng Gao, Gangshan Wu, Limin Wang CVPR2023 2023/9/27

2. ◼End-to-End STMixer •

3. ◼ • ◼ • [Carion+, ECCV2020] End-to-End • •

4. ASAM module

5. ◼ ◼ ◼ • box 𝑥𝑛, 𝑦𝑛, 𝑧𝑛, 𝑟𝑛 • 𝑥𝑛, 𝑦𝑛 • Box • 𝑧𝑛 • box • 𝑟𝑛 •

6. 4 ◼ • •

7. ASAM module ASAM module

8. ASAM module ◼ 𝑄𝑠 𝑄𝑡 self-attention • ◼ • Adaptive Spatiotemporal Feature Sampling • Adaptive Dual-branch Feature Mixing ◼ 𝑄𝑝 ′ ,𝑄𝑠 ′ ,𝑄𝑡 ′ ASAM module

9. Adaptive Spatiotemporal Feature Sampling ◼𝑄𝑠 𝑛 box • 𝑃𝑖𝑛 ◼ • 𝑇 × 𝑃𝑖𝑛 ◼4 • F𝑛 ∈ 𝑅𝑇×𝑃𝑖𝑛×𝐷

10. Adaptive Dual-branch Feature Mixing ◼ • • • • 𝑀𝑐

11. Adaptive Dual-branch Feature Mixing ◼ 𝑀𝑝

12. ◼Human score • box • ◼Action score • • ◼Human boxes • Bounding box •

13. ◼ L 𝐿𝑠𝑒𝑡 ℒ𝑎𝑐𝑡 ◼ℒ𝑐𝑙𝑠 2 ◼ℒ𝐿1 ℒ𝑔𝑖𝑜𝑢 box

14. ◼ • AVA [Gu+, CVP2018] ◼ • D • 256 • N • 100 ◼ • 𝜆𝑐𝑙𝑠 = 2.0, 𝜆𝐿1= 2.0 • 𝜆𝑔𝑖𝑜𝑢 = 2.0, 𝜆𝑎𝑐𝑡 = 24.0 ◼Optimizer • AdamW • • 1e-4 ◼ • 16 ◼ • 10 ◼ • Mean Average Precision(mAP) • IoU 0.5

15. Ablation Study ◼ • 4 • • ◼ • 4 •

16. Ablation Study ◼ • RoIAlign [He+, ICCV2017] • box • • ◼ • 4 •

17. Ablation Study ◼ ASAM module ◼ • • • 32 • • 100 • ASAM module • 6

18. Ablation Study ◼ ◼ •

19. State-of-the-arts ◼

20. JHMDB UCF101-24 ◼JHMDB [Jhuang+, ICCV2013] UCF101-24 [Singh+, ICCV2017] ◼ • JHMDB • UCF101-24

21. Visualization ◼ • • •

22. ◼End-to-End STMixer ◼AVA,JHMDB,UCF101-24 ◼ •