In this paper, we briefly introduce the solution developed by our team, HFUT-VUT, for the track of Micro-gesture Classification in the MiGA challenge at IJCAI 2024. The task of micro-gesture classification task involves recognizing the category of a given video clip, which focuses on more fine-grained and subtle body movements compared to typical action recognition tasks. Given the inherent complexity of micro-gesture recognition, which includes large intra-class variability and minimal inter-class differences, we utilize two innovative modules, i.e., the cross-modal fusion module and prototypical refinement module, to improve the discriminative ability of MG features, thereby improving the classification accuracy. Our solution achieved significant success, ranking 1st in the track of Micro-gesture Classification. We surpassed the performance of last year's leading team by a substantial margin, improving Top-1 accuracy by 6.13%.

翻译：本文简要介绍了我们团队HFUT-VUT为IJCAI 2024 MiGA挑战赛中微手势分类赛道所开发的解决方案。微手势分类任务旨在识别给定视频片段的类别，其关注的身体动作比典型的动作识别任务更为细粒度和微妙。鉴于微手势识别固有的复杂性——包括较大的类内变异性和极小的类间差异，我们采用了两个创新模块，即跨模态融合模块和原型精化模块，以提升微手势特征的判别能力，从而提高分类准确率。我们的解决方案取得了显著成功，在微手势分类赛道中排名第一。我们以较大优势超越了去年领先团队的性能，将Top-1准确率提升了6.13%。