标题： 基于数据增强和时空注意力的在线微手势识别 显示英文标题

标题： Online Micro-gesture Recognition Using Data Augmentation and Spatial-Temporal Attention

摘要： 在本文中，我们介绍了由我们的团队开发的最新解决方案，HFUT-VUT，用于IJCAI 2025 MiGA挑战的微动作在线识别赛道。 微动作在线识别任务是一个极具挑战性的问题，旨在定位未剪辑视频中多个微动作实例的时间位置并识别其类别。 与传统的时序动作检测相比，该任务更强调区分微动作类别并精确识别每个实例的开始和结束时间。 此外，微动作通常是自发的人类动作，其差异性大于其他人类动作。 为了解决这些挑战，我们提出了手工设计的数据增强和时空注意力机制，以提高模型对微动作进行分类和定位的准确性。 我们的解决方案获得了38.03的F1分数，比之前的最先进方法提高了37.9%。 因此，我们的方法在微动作在线识别赛道中排名第一。 显示英文摘要

摘要： 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.