自监督学习提升微手势识别精度，简单集成即达新顶尖水平。
Self-supervised Learning Matters: A Simple Ensemble Solution for Micro-Gesture Recognition
- 用无标签视频预训练自监督RGB模型，再微调至iMiGUE数据集。
- 单模型达69.224%准确率，集成后提升至74.419%，超旧最优1.206个百分点。
- 适合关注小样本手势识别与自监督表征学习的研究者。
本文介绍XInsight Lab在IJCAI 2026第四届MiGA挑战赛微手势分类赛道的解决方案，该方案排名第一，并创下新基准。我们提出一种多模态集成框架，将自监督的RGB模型与先前方案中的监督多流模型结合。自监督RGB模型在12万条未标注视频片段上通过掩码视频建模预训练，随后在iMiGUE数据集上微调，取得iMiGUE测试集69.224%的top-1准确率，验证了从领域内无标签视频中学习可迁移表征的有效性。通过将其作为互补分支加入集成，最终模型达到74.419% top-1准确率，较此前最优结果提升1.206个百分点。在iMiGUE上的实验，包括对集成策略的消融研究，证实了自监督RGB表征学习在微手势识别中的有效性。
原文摘要 · Abstract (English)
In this paper, we present XInsight Lab's solution to the micro-gesture classification track of the 4th MiGA Challenge at IJCAI 2026, in which our solution ranked first and achieved a new state-of-the-art result. We propose a multimodal ensemble framework that integrates a self-supervised RGB-based model with supervised multi-stream models from previous solutions. The self-supervised RGB model is pretrained on 120K unlabeled clips via masked video modeling and then fine-tuned on iMiGUE. This simple yet effective RGB baseline achieves 69.224% top-1 accuracy on the iMiGUE test set, demonstrating the benefit of learning transferable representations from unlabeled in-domain videos. By incorporating this model as a complementary branch, the final ensemble reaches 74.419% top-1 accuracy, surpassing the previous state of the art by 1.206 percentage points. Experimental results on iMiGUE, including ablation studies on the ensemble strategy, validate the effectiveness of self-supervised RGB representation learning for micro-gesture recognition.
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