Title: LSTC-MDA: A Unified Framework for Long-Short Term Temporal Convolution and Mixed Data Augmentation in Skeleton-Based Action Recognition Show Chinese title

Title: LSTC-MDA：基于骨架动作识别的长期短期时间卷积和混合数据增强的统一框架

Abstract: Skeleton-based action recognition faces two longstanding challenges: the scarcity of labeled training samples and difficulty modeling short- and long-range temporal dependencies. To address these issues, we propose a unified framework, LSTC-MDA, which simultaneously improves temporal modeling and data diversity. We introduce a novel Long-Short Term Temporal Convolution (LSTC) module with parallel short- and long-term branches, these two feature branches are then aligned and fused adaptively using learned similarity weights to preserve critical long-range cues lost by conventional stride-2 temporal convolutions. We also extend Joint Mixing Data Augmentation (JMDA) with an Additive Mixup at the input level, diversifying training samples and restricting mixup operations to the same camera view to avoid distribution shifts. Ablation studies confirm each component contributes. LSTC-MDA achieves state-of-the-art results: 94.1% and 97.5% on NTU 60 (X-Sub and X-View), 90.4% and 92.0% on NTU 120 (X-Sub and X-Set),97.2% on NW-UCLA. Code: https://github.com/xiaobaoxia/LSTC-MDA. Show Chinese abstract

Abstract: 基于骨架的动作识别面临两个长期挑战：标记训练样本的稀缺性和难以建模短时和长时时间依赖性。 为了解决这些问题，我们提出了一种统一框架LSTC-MDA，该框架同时改进时间建模和数据多样性。 我们引入了一个新颖的长短时序卷积（LSTC）模块，具有并行的短时和长时分支，这两个特征分支随后通过学习到的相似性权重自适应地对齐和融合，以保留传统步长-2时间卷积丢失的关键长时线索。 我们还通过在输入级别添加Additive Mixup扩展了联合混合数据增强（JMDA），多样化训练样本，并将混合操作限制在同一摄像头视角以避免分布偏移。 消融研究证实了每个组件的贡献。 LSTC-MDA取得了最先进的结果：在NTU 60（X-Sub和X-View）上分别为94.1%和97.5%，在NTU 120（X-Sub和X-Set）上分别为90.4%和92.0%，在NW-UCLA上为97.2%。 代码：https://github.com/xiaobaoxia/LSTC-MDA.