5 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.CV2024
Shap-Mix: Shapley Value Guided Mixing for Long-Tailed Skeleton Based Action Recognition
Jiahang Zhang, Lilang Lin, Jiaying Liu
In real-world scenarios, human actions often fall into a long-tailed distribution. It makes the existing skeleton-based action recognition works, which are mostly designed based on…
cs.CV2023
Prompted Contrast with Masked Motion Modeling: Towards Versatile 3D Action Representation Learning
Jiahang Zhang, Lilang Lin, Jiaying Liu
Self-supervised learning has proved effective for skeleton-based human action understanding, which is an important yet challenging topic. Previous works mainly rely on contrastive…
cs.CV2023★ 5 cited
Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition
Lilang Lin, Jiahang Zhang, Jiaying Liu
The self-supervised pretraining paradigm has achieved great success in skeleton-based action recognition. However, these methods treat the motion and static parts equally, and lack…