Joint Skeletal and Semantic Embedding Loss for Micro-gesture Classification

Kun Li, Dan Guo, Guoliang Chen, Xinge Peng, Meng Wang
7/20/2023
cs.CV

No official code found. Best implementation: VUT-HFUT/MiGA2023_Track1 (9 stars, Apache-2.0, last commit 2024-01-21).

Abstract

In this paper, we briefly introduce the solution of our team HFUT-VUT for the Micros-gesture Classification in the MiGA challenge at IJCAI 2023. The micro-gesture classification task aims at recognizing the action category of a given video based on the skeleton data. For this task, we propose a 3D-CNNs-based micro-gesture recognition network, which incorporates a skeletal and semantic embedding loss to improve action classification performance. Finally, we rank 1st in the Micro-gesture Classification Challenge, surpassing the second-place team in terms of Top-1 accuracy by 1.10%.

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Code Implementations(1)

VUT-HFUT/MiGA2023_Track1Community100%
Apache-2.0

[IJCAI 2023] 🏆 The Champion of Micro-gesture Classification sub-challenge in MiGA@IJCAI2023.

92Shell, PythonAug 4, 20232 years agoApache-2.0

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Cite this paper

@article{li2023joint,
  title  = {Joint Skeletal and Semantic Embedding Loss for Micro-gesture Classification},
  author = {Kun Li and Dan Guo and Guoliang Chen and Xinge Peng and Meng Wang},
  year   = {2023},
  eprint = {2307.10624},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2307.10624v1}
}

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