Papers › PaStaNet: Toward Human Activity Knowledge Engine
PaStaNet: Toward Human Activity Knowledge Engine
Yong-Lu Li, Liang Xu, Xinpeng Liu, Xijie Huang, Yue Xu, Shiyi Wang, Hao-Shu Fang, Ze Ma, Mingyang Chen, Cewu Lu
Existing image-based activity understanding methods mainly adopt direct mapping, i.e. from image to activity concepts, which may encounter performance bottleneck since the huge gap. In light of this, we propose a new path: infer human part states first and then reason out the activities based on part-level semantics. Human Body Part States (PaSta) are fine-grained action semantic tokens, e.g. <hand, hold, something>, which can compose the activities and help us step toward human activity knowledge engine. To fully utilize the power of PaSta, we build a large-scale knowledge base PaStaNet, which contains 7M+ PaSta annotations. And two corresponding models are proposed: first, we design a model named Activity2Vec to extract PaSta features, which aim to be general representations for various activities. Second, we use a PaSta-based Reasoning method to infer activities. Promoted by PaStaNet, our method achieves significant improvements, e.g. 6.4 and 13.9 mAP on full and one-shot sets of HICO in supervised learning, and 3.2 and 4.2 mAP on V-COCO and images-based AVA in transfer learning. Code and data are available at http://hake-mvig.cn/.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Human-Object Interaction Detection | HICO | PaStaNet | mAP | 46.3 | #3 of 8 | Archive leaderboard | report |
| Human-Object Interaction Detection | HICO-DET | PaStaNet | mAP | 22.65 | #40 of 55 | Archive leaderboard | report |
| Human-Object Interaction Detection | V-COCO | PaStaNet | AP(S1) | 51.0 | #26 of 34 | Archive leaderboard | report |
| Human-Object Interaction Detection | V-COCO | PaStaNet | AP(S2) | 57.5 | #26 of 34 | Archive leaderboard | report |
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