Papers › Joint Skeletal and Semantic Embedding Loss for Micro-gesture Classification
Joint Skeletal and Semantic Embedding Loss for Micro-gesture Classification
Kun Li, Dan Guo, Guoliang Chen, Xinge Peng, Meng Wang
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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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Micro-gesture Recognition | iMiGUE | Top 1 Accuracy | 64.12 | #1 of 1 | Archive leaderboard | report | |
| Micro-gesture Recognition | iMiGUE | Top 5 Accuracy | 91.1 | #1 of 1 | Archive leaderboard | report |
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