Online Micro-gesture Recognition Using Data Augmentation and Spatial-Temporal Attention
This work addresses the challenging task of online micro-gesture recognition, which is important for applications in human-computer interaction, but it appears incremental as it builds on existing methods with specific enhancements.
The paper tackled the problem of recognizing and localizing multiple micro-gesture instances in untrimmed videos, achieving an F1 score of 38.03 and outperforming the previous state-of-the-art by 37.9% to rank first in the IJCAI 2025 MiGA Challenge.
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.