Spatial-Temporal Decoupled Adapter for Micro-gesture Online Recognition
For researchers in video understanding and gesture recognition, it addresses the challenge of capturing fine-grained spatiotemporal patterns in micro-gestures.
The paper tackles micro-gesture online recognition in untrimmed videos, achieving an F1 score of 0.43808 and ranking 1st in Track 2 of the 4th EI-MiGA-IJCAI Challenge.
Micro-gesture online recognition aims to temporally localize and classify subtle gestures in untrimmed videos. Owing to their extremely short duration, low motion amplitude, and ambiguous visual cues, capturing discriminative spatiotemporal representations remains highly challenging. Existing parameter-efficient adapters typically employ a single branch to model spatial and temporal cues jointly, which may fail to capture the fine-grained patterns of micro-gestures. To address this limitation, we propose a Spatial-Temporal Decoupled Adapter that decomposes video adaptation into independent temporal and spatial branches via lightweight depthwise convolutions. In addition, to address the long-tail distribution problem in the benchmark dataset, we introduce Adaptive Soft Balanced Augmentation, which dynamically allocates augmentation intensity based on class rarity and learning difficulty, without manual thresholds. Our method achieves an F1 score of 0.43808, ranking 1st in Track 2 of the 4th EI-MiGA-IJCAI Challenge.