3 citations · 4 across the 4 of their papers we have counts for
4 papers
Affinity Contrastive Learning for Skeleton-based Human Activity Understanding
Hongda Liu, Yunfan Liu, Min Ren +3
In skeleton-based human activity understanding, existing methods often adopt the contrastive learning paradigm to construct a discriminative feature space. However, many of these a…
SkeletonAgent: An Agentic Interaction Framework for Skeleton-based Action Recognition
Hongda Liu, Yunfan Liu, Changlu Wang +2
Recent advances in skeleton-based action recognition increasingly leverage semantic priors from Large Language Models (LLMs) to enrich skeletal representations. However, the LLM is…
Revealing Key Details to See Differences: A Novel Prototypical Perspective for Skeleton-based Action Recognition
Hongda Liu, Yunfan Liu, Min Ren +3
In skeleton-based action recognition, a key challenge is distinguishing between actions with similar trajectories of joints due to the lack of image-level details in skeletal repre…
Balanced Representation Learning for Long-tailed Skeleton-based Action Recognition
Hongda Liu, Yunlong Wang, Min Ren +4
Skeleton-based action recognition has recently made significant progress. However, data imbalance is still a great challenge in real-world scenarios. The performance of current act…