Technical Report: Disentangled Action Parsing Networks for Accurate Part-level Action Parsing

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ID: 282430
2021
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Abstract
Part-level Action Parsing aims at part state parsing for boosting action recognition in videos. Despite of dramatic progresses in the area of video classification research, a severe problem faced by the community is that the detailed understanding of human actions is ignored. Our motivation is that parsing human actions needs to build models that focus on the specific problem. We present a simple yet effective approach, named disentangled action parsing (DAP). Specifically, we divided the part-level action parsing into three stages: 1) person detection, where a person detector is adopted to detect all persons from videos as well as performs instance-level action recognition; 2) Part parsing, where a part-parsing model is proposed to recognize human parts from detected person images; and 3) Action parsing, where a multi-modal action parsing network is used to parse action category conditioning on all detection results that are obtained from previous stages. With these three major models applied, our approach of DAP records a global mean of $0.605$ score in 2021 Kinetics-TPS Challenge.
Reference Key
song2021technical Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Xuanhan Wang; Xiaojia Chen; Lianli Gao; Lechao Chen; Jingkuan Song
Journal arXiv
Year 2021
DOI
DOI not found
URL
http://arxiv.org/abs/2111.03225v1
Keywords
cs.cv

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