A hierarchical spatiotemporal vector quantization framework segments skeleton-based actions without supervision, achieving new state-of-the-art results on HuGaDB, LARa, and BABEL while reducing segment length bias.
arXiv preprint arXiv:2204.10312 (2022)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Unsupervised Skeleton-Based Action Segmentation via Hierarchical Spatiotemporal Vector Quantization
A hierarchical spatiotemporal vector quantization framework segments skeleton-based actions without supervision, achieving new state-of-the-art results on HuGaDB, LARa, and BABEL while reducing segment length bias.