Paper Info
Title
PaStaNet: Toward Human Activity Knowledge Engine
Abstract
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/.
Year
DOI
Venue
2020
10.1109/CVPR42600.2020.00046
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Keywords
DocType
ISSN
human activity knowledge engine,activity concepts,infer human part states,part-level semantics,Human Body Part States,large-scale knowledge base PaStaNet,Activity2Vec,PaSta features,PaSta annotations,V-COCO,images-based AVA,HICO,supervised learning,PaSta-based reasoning method
Conference
1063-6919
ISBN
Citations 
PageRank 
978-1-7281-7169-2
0
0.34
References 
Authors
44
10
Name
Order
Citations
PageRank
Yonglu Li1227.05
Liang Xu2192.26
Xinpeng Liu351.75
Xijie Huang4192.26
Xu Yue500.34
Wang Shiyi610.69
Haoshu Fang7576.86
Ze Ma8130.84
Chen Mingyang900.34
Cewu Lu1099362.08