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20162024
most citedFeature Alignment and Restoration for Domain Generalization and Adaptation

35 citations · 141 across the 18 of their papers we have counts for

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Showing 2020Show all

7 papers · 1 filter

cs.CV202019 cited

Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification

Kecheng Zheng, Cuiling Lan, Wenjun Zeng +2

Many unsupervised domain adaptive (UDA) person re-identification (ReID) approaches combine clustering-based pseudo-label prediction with feature fine-tuning. However, because of do…

cs.CV20207 cited

Global Distance-distributions Separation for Unsupervised Person Re-identification

Xin Jin, Cuiling Lan, Wenjun Zeng +1

Supervised person re-identification (ReID) often has poor scalability and usability in real-world deployments due to domain gaps and the lack of annotations for the target domain d…

cs.CV202035 cited

Feature Alignment and Restoration for Domain Generalization and Adaptation

Xin Jin, Cuiling Lan, Wenjun Zeng +1

For domain generalization (DG) and unsupervised domain adaptation (UDA), cross domain feature alignment has been widely explored to pull the feature distributions of different doma…

cs.CV202021 cited

Style Normalization and Restitution for Generalizable Person Re-identification

Xin Jin, Cuiling Lan, Wenjun Zeng +2

Existing fully-supervised person re-identification (ReID) methods usually suffer from poor generalization capability caused by domain gaps. The key to solving this problem lies in…

cs.CV2020

Multi-Granularity Reference-Aided Attentive Feature Aggregation for Video-based Person Re-identification

Zhizheng Zhang, Cuiling Lan, Wenjun Zeng +1

Video-based person re-identification (reID) aims at matching the same person across video clips. It is a challenging task due to the existence of redundancy among frames, newly rev…

cs.CV2020

STC-Flow: Spatio-temporal Context-aware Optical Flow Estimation

Xiaolin Song, Yuyang Zhao, Jingyu Yang

In this paper, we propose a spatio-temporal contextual network, STC-Flow, for optical flow estimation. Unlike previous optical flow estimation approaches with local pyramid feature…