activity
20162022
most citedLearning Deep Context-aware Features over Body and Latent Parts for Person Re-identification

90 citations · 158 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20211 cited

Spatial and Semantic Consistency Regularizations for Pedestrian Attribute Recognition

Jian Jia, Xiaotang Chen, Kaiqi Huang

While recent studies on pedestrian attribute recognition have shown remarkable progress in leveraging complicated networks and attention mechanisms, most of them neglect the inter-…

cs.CV202030 cited

Rethinking of Pedestrian Attribute Recognition: Realistic Datasets with Efficient Method

Jian Jia, Houjing Huang, Wenjie Yang +2

Despite various methods are proposed to make progress in pedestrian attribute recognition, a crucial problem on existing datasets is often neglected, namely, a large number of iden…

cs.CV201835 cited

EANet: Enhancing Alignment for Cross-Domain Person Re-identification

Houjing Huang, Wenjie Yang, Xiaotang Chen +5

Person re-identification (ReID) has achieved significant improvement under the single-domain setting. However, directly exploiting a model to new domains is always faced with huge…

cs.CV201790 cited

Learning Deep Context-aware Features over Body and Latent Parts for Person Re-identification

Dangwei Li, Xiaotang Chen, Zhang Zhang +1

Person Re-identification (ReID) is to identify the same person across different cameras. It is a challenging task due to the large variations in person pose, occlusion, background…

cs.CV2016

A Richly Annotated Dataset for Pedestrian Attribute Recognition

Dangwei Li, Zhang Zhang, Xiaotang Chen +2

In this paper, we aim to improve the dataset foundation for pedestrian attribute recognition in real surveillance scenarios. Recognition of human attributes, such as gender, and cl…