34 citations · 34 across the 5 of their papers we have counts for
5 papers
Cross-Domain Attribute Alignment with CLIP: A Rehearsal-Free Approach for Class-Incremental Unsupervised Domain Adaptation
Kerun Mi, Guoliang Kang, Guangyu Li +3
Class-Incremental Unsupervised Domain Adaptation (CI-UDA) aims to adapt a model from a labeled source domain to an unlabeled target domain, where the sets of potential target class…
Pre-training a Density-Aware Pose Transformer for Robust LiDAR-based 3D Human Pose Estimation
Xiaoqi An, Lin Zhao, Chen Gong +2
With the rapid development of autonomous driving, LiDAR-based 3D Human Pose Estimation (3D HPE) is becoming a research focus. However, due to the noise and sparsity of LiDAR-captur…
Domain adaptive pose estimation via multi-level alignment
Yugan Chen, Lin Zhao, Yalong Xu +3
Domain adaptive pose estimation aims to enable deep models trained on source domain (synthesized) datasets produce similar results on the target domain (real-world) datasets. The e…
A comprehensive framework for occluded human pose estimation
Linhao Xu, Lin Zhao, Xinxin Sun +3
Occlusion presents a significant challenge in human pose estimation. The challenges posed by occlusion can be attributed to the following factors: 1) Data: The collection and annot…
SHaRPose: Sparse High-Resolution Representation for Human Pose Estimation
Xiaoqi An, Lin Zhao, Chen Gong +3
High-resolution representation is essential for achieving good performance in human pose estimation models. To obtain such features, existing works utilize high-resolution input im…