35 citations · 141 across the 18 of their papers we have counts for
8 papers · 1 filter
Multi-Scale Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition
Pengfei Zhang, Cuiling Lan, Wenjun Zeng +3
Skeleton data is of low dimension. However, there is a trend of using very deep and complicated feedforward neural networks to model the skeleton sequence without considering the c…
Pose-Guided Feature Learning with Knowledge Distillation for Occluded Person Re-Identification
Kecheng Zheng, Cuiling Lan, Wenjun Zeng +3
Occluded person re-identification (ReID) aims to match person images with occlusion. It is fundamentally challenging because of the serious occlusion which aggravates the misalignm…
ToAlign: Task-oriented Alignment for Unsupervised Domain Adaptation
Guoqiang Wei, Cuiling Lan, Wenjun Zeng +2
Unsupervised domain adaptive classifcation intends to improve the classifcation performance on unlabeled target domain. To alleviate the adverse effect of domain shift, many approa…
PlayVirtual: Augmenting Cycle-Consistent Virtual Trajectories for Reinforcement Learning
Tao Yu, Cuiling Lan, Wenjun Zeng +3
Learning good feature representations is important for deep reinforcement learning (RL). However, with limited experience, RL often suffers from data inefficiency for training. For…
Disentanglement-based Cross-Domain Feature Augmentation for Effective Unsupervised Domain Adaptive Person Re-identification
Zhizheng Zhang, Cuiling Lan, Wenjun Zeng +4
Unsupervised domain adaptive (UDA) person re-identification (ReID) aims to transfer the knowledge from the labeled source domain to the unlabeled target domain for person matching.…
MetaAlign: Coordinating Domain Alignment and Classification for Unsupervised Domain Adaptation
Guoqiang Wei, Cuiling Lan, Wenjun Zeng +1
For unsupervised domain adaptation (UDA), to alleviate the effect of domain shift, many approaches align the source and target domains in the feature space by adversarial learning…