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%.