5 citations · 12 across the 9 of their papers we have counts for
9 papers
Idempotent Unsupervised Representation Learning for Skeleton-Based Action Recognition
Lilang Lin, Lehong Wu, Jiahang Zhang +1
Generative models, as a powerful technique for generation, also gradually become a critical tool for recognition tasks. However, in skeleton-based action recognition, the features…
MacDiff: Unified Skeleton Modeling with Masked Conditional Diffusion
Lehong Wu, Lilang Lin, Jiahang Zhang +2
Self-supervised learning has proved effective for skeleton-based human action understanding. However, previous works either rely on contrastive learning that suffers false negative…
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…
Self-Supervised Skeleton-Based Action Representation Learning: A Benchmark and Beyond
Jiahang Zhang, Lilang Lin, Shuai Yang +1
Self-supervised learning (SSL), which aims to learn meaningful prior representations from unlabeled data, has been proven effective for skeleton-based action understanding. Differe…
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…
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…
Self-supervised Training Sample Difficulty Balancing for Local Descriptor Learning
Jiahan Zhang, Dayong Tian
In the case of an imbalance between positive and negative samples, hard negative mining strategies have been shown to help models learn more subtle differences between positive and…
Hierarchical Consistent Contrastive Learning for Skeleton-Based Action Recognition with Growing Augmentations
Jiahang Zhang, Lilang Lin, Jiaying Liu
Contrastive learning has been proven beneficial for self-supervised skeleton-based action recognition. Most contrastive learning methods utilize carefully designed augmentations to…
SMars: Semi-Supervised Learning for Mars Semantic Segmentation
Jiahang Zhang, Lilang Lin, Zejia Fan +2
Deep learning has become a powerful tool for Mars exploration. Mars terrain semantic segmentation is an important Martian vision task, which is the base of rover autonomous plannin…