# A Baseline Framework for Part-level Action Parsing and Action Recognition

Source: [A Baseline Framework for Part-level Action Parsing and Action Recognition](<https://www.sota2.com/research/paper/68fa6d571e209131cea39466>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:32:23.745Z

Authors: Xiaodong Chen, Xinchen Liu, Kun Liu, Wu Liu, Tao Mei

Published: 2021-10-07T12:04:59.000Z

## Abstract

This technical report introduces our 2nd place solution to Kinetics-TPS Track on Part-level Action Parsing in ICCV DeeperAction Workshop 2021. Our entry is mainly based on YOLOF for instance and part detection, HRNet for human pose estimation, and CSN for video-level action recognition and frame-level part state parsing. We describe technical details for the Kinetics-TPS dataset, together with some experimental results. In the competition, we achieved 61.37% mAP on the test set of Kinetics-TPS.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2110.03368>)

[PDF](<https://arxiv.org/pdf/2110.03368.pdf>)