Joint Skeletal and Semantic Embedding Loss for Micro-gesture Classification
This work addresses micro-gesture recognition for human-computer interaction, but it is incremental as it builds on existing methods for a specific competition.
The paper tackled micro-gesture classification from skeleton data by proposing a 3D-CNN network with a joint skeletal and semantic embedding loss, achieving first place in a challenge with a 1.10% Top-1 accuracy improvement over the second-place team.
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%.