Data Augmentation for Human Behavior Analysis in Multi-Person
  Conversations
Kun Li Dan Guo Guoliang Chen Feiyang Liu Meng Wang
In this paper, we present the solution of our team HFUT-VUT for the
MultiMediate Grand Challenge 2023 at ACM Multimedia 2023. The solution covers
three sub-challenges: bodily behavior recognition, eye contact detection, and
next speaker prediction. We select Swin Transformer as the baseline and exploit
data augmentation strategies to address the above three tasks. Specifically, we
crop the raw video to remove the noise from other parts. At the same time, we
utilize data augmentation to improve the generalization of the model. As a
result, our solution achieves the best results of 0.6262 for bodily behavior
recognition in terms of mean average precision and the accuracy of 0.7771 for
eye contact detection on the corresponding test set. In addition, our approach
also achieves comparable results of 0.5281 for the next speaker prediction in
terms of unweighted average recall.