208 citations · 224 across the 11 of their papers we have counts for
11 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…
Coding for Intelligence from the Perspective of Category
Wenhan Yang, Zixuan Hu, Lilang Lin +2
Coding, which targets compressing and reconstructing data, and intelligence, often regarded at an abstract computational level as being centered around model learning and predictio…
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…
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…
Semi-Supervised Learning for Mars Imagery Classification and Segmentation
Wenjing Wang, Lilang Lin, Zejia Fan +1
With the progress of Mars exploration, numerous Mars image data are collected and need to be analyzed. However, due to the imbalance and distortion of Martian data, the performance…
MSL: Multi-Task Self-Supervised Learning for Skeleton Based Action Recognition
Lilang Lin, Sijie Song, Wenhan Yan +1
In this paper, we address self-supervised representation learning from human skeletons for action recognition. Previous methods, which usually learn feature presentations from a si…