Abstract
In this paper, we introduce the latest solution developed by our team, HFUT-VUT, for the Micro-gesture Online Recognition track of the IJCAI 2025 MiGA Challenge. The Micro-gesture Online Recognition task is a highly challenging problem that aims to locate the temporal positions and recognize the categories of multiple micro-gesture instances in untrimmed videos. Compared to traditional temporal action detection, this task places greater emphasis on distinguishing between micro-gesture categories and precisely identifying the start and end times of each instance. Moreover, micro-gestures are typically spontaneous human actions, with greater differences than those found in other human actions. To address these challenges, we propose hand-crafted data augmentation and spatial-temporal attention to enhance the model's ability to classify and localize micro-gestures more accurately. Our solution achieved an F1 score of 38.03, outperforming the previous state-of-the-art by 37.9%. As a result, our method ranked first in the Micro-gesture Online Recognition track.
Abstract (translated)
在这篇论文中，我们介绍了由我们团队HFUT-VUT为IJCAI 2025 MiGA挑战赛中的Micro-gesture Online Recognition（微手势在线识别）赛道开发的最新解决方案。微手势在线识别任务是一个极具挑战性的难题，其目标是在未剪辑的视频中定位多个微手势实例的时间位置并识别它们的类别。与传统的时序动作检测相比，该任务更侧重于区分不同的微手势类别，并精确地确定每个实例的开始和结束时间。此外，微手势通常是自发的人类行为，在细微差别上比其他人类动作更为不同。为了应对这些挑战，我们提出了手工设计的数据增强方法以及空间-时间注意力机制来提升模型对微手势分类与定位的能力。我们的解决方案取得了38.03的F1分数，超过了之前的最先进水平37.9%。因此，在微手势在线识别赛道上，我们的方法排名第一。
URL
https://arxiv.org/abs/2507.09512
