  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.
