通过分层时空向量量化，无监督地分割骨架动作序列。
Unsupervised Skeleton-Based Action Segmentation via Hierarchical Spatiotemporal Vector Quantization
- 分两层量化：先分细粒度子动作，再聚合成动作级表征。
- 在多个数据集上超越现有方法，减少片段长度偏差。
- 适合无标注骨架动作分割任务，尤其关注时序结构建模。
我们提出一种新颖的分层时空向量量化框架，用于无监督的骨架动作时间分割。首先引入分层策略，包含两个连续的向量量化层级：低层将骨架关联到细粒度子动作，高层进一步将子动作聚合为动作级表示。该分层方法优于非分层基线，主要依赖空间线索重建输入骨架。随后，通过融合空间与时间信息，构建分层时空向量量化方案。该方法实现多层级聚类，同时恢复输入骨架及其对应的时间戳。在HuGaDB、LARa和BABEL等多个基准上的大量实验表明，该方法达到新最优性能，并有效降低无监督骨架动作分割中的片段长度偏差。
原文摘要 · Abstract (English)
We propose a novel hierarchical spatiotemporal vector quantization framework for unsupervised skeleton-based temporal action segmentation. We first introduce a hierarchical approach, which includes two consecutive levels of vector quantization. Specifically, the lower level associates skeletons with fine-grained subactions, while the higher level further aggregates subactions into action-level representations. Our hierarchical approach outperforms the non-hierarchical baseline, while primarily exploiting spatial cues by reconstructing input skeletons. Next, we extend our approach by leveraging both spatial and temporal information, yielding a hierarchical spatiotemporal vector quantization scheme. In particular, our hierarchical spatiotemporal approach performs multi-level clustering, while simultaneously recovering input skeletons and their corresponding timestamps. Lastly, extensive experiments on multiple benchmarks, including HuGaDB, LARa, and BABEL, demonstrate that our approach establishes a new state-of-the-art performance and reduces segment length bias in unsupervised skeleton-based temporal action segmentation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。