CVPR 2020poster190 citations
PaStaNet: Toward Human Activity Knowledge Engine
Yong-Lu Li, Liang Xu, Xinpeng Liu, Xijie Huang, Yue Xu, Shiyi Wang, Hao-Shu Fang, Ze Ma
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.
BibTeX
@inproceedings{cvpr2020_pastanettowardhu,
title = {PaStaNet: Toward Human Activity Knowledge Engine},
author = {Yong-Lu Li and Liang Xu and Xinpeng Liu and Xijie Huang and Yue Xu and Shiyi Wang and Hao-Shu Fang and Ze Ma and Mingyang Chen and Cewu Lu},
booktitle = {CVPR 2020},
year = {2020}
}