提出新模型与数据集，精准定位羽毛球比赛中的细微动作。
Decoupling Spatio-Temporal Adapter for Fine-Grained Badminton Action Localization
- 拆分时空特征为三路并行，高效捕捉细微运动模式。
- 在27597个标注动作上达到顶尖定位精度，参数增长极小。
- 适合研究细粒度动作识别、体育视频分析的学者和工程师。
时间动作定位（TAL）在通用视频理解中已广泛研究，但专业羽毛球等细粒度运动场景因复杂的时空动态仍研究不足。本文聚焦专业羽毛球视频中的细粒度TAL，提出新基准数据集Fine-Badminton，包含31场赛事、29种精细击球类别，覆盖2104次回合与27597个标注动作。为有效建模此类场景的复杂运动模式，提出解耦时空适配器（DSTA），在参数高效的框架内实现时空特征建模。DSTA将运动表示分解为三条并行分支，分别捕捉时间动态及垂直、水平空间变化，提升对细微动作差异的区分能力。在Fine-Badminton与ShuttleSet两个数据集上的大量实验表明，该方法达到当前最优性能，计算与参数开销仅略有增加。结果验证了该方法在细粒度时间动作定位中的有效性与高效性。
原文摘要 · Abstract (English)
Temporal Action Localization (TAL) has been extensively studied in generic video understanding, while fine-grained sports scenarios, such as professional badminton, remain underexplored due to their complex and subtle spatio-temporal dynamics. In this paper, we focus on fine-grained TAL in professional badminton videos and introduce a new benchmark dataset, Fine-Badminton, which consists of 31 matches with 29 fine-grained stroke categories, covering 2104 rallies and 27597 annotated actions. To effectively capture the intricate motion patterns in such scenarios, we propose a Decoupling Spatio-Temporal Adapter (DSTA), which enables efficient modeling of spatio-temporal features within a parameter-efficient framework. Specifically, DSTA decomposes motion representation into three parallel branches, capturing temporal dynamics as well as vertical and horizontal spatial variations. The design allows the model to better distinguish subtle differences among fine-grained actions. Extensive experiments on both the Fine-Badminton dataset and the ShuttleSet benchmark demonstrate that the proposed method achieves state-of-the-art performance while introducing only a marginal increase in computational and parameter cost. These results validate the effectiveness and efficiency of the proposed approach for fine-grained temporal action localization.
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