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.

我们的研究团队 HFUT-VUT 在 ACM Multimedia 2023 的 MultiMediate Grand Challenge 2023 中的解决方案覆盖了三个子挑战：身体行为识别，眼神接触检测和下一个发言者的预测。我们选择 Swin Transformer 作为基线，并利用数据增广策略来解决上述三个任务。具体来说，我们裁剪原始视频以去除其他部分的噪声。同时，我们利用数据增广来提高模型的泛化能力。结果，我们的解决方案在平均精度均值方面对身体行为识别取得了最佳结果 0.6262，并在相应的测试集上的眼神接触检测精确度方面获得了 0.7771 的准确率。此外，我们的方法在下一个发言者的预测方面也实现了 0.5281 的不加权平均召回率的可比较结果。