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20162022
most citedPerson Re-identification: Past, Present and Future

1k citations · 2.8k across the 28 of their papers we have counts for

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Showing 2019 · cs.CVShow all

6 papers · 2 filters

cs.CV2019★ 3 cited

Very Long Natural Scenery Image Prediction by Outpainting

Zongxin Yang, Jian Dong, Ping Liu +2

Comparing to image inpainting, image outpainting receives less attention due to two challenges in it. The first challenge is how to keep the spatial and content consistency between…

cs.CV2019

Unsupervised Scene Adaptation with Memory Regularization in vivo

Zhedong Zheng, Yi Yang

We consider the unsupervised scene adaptation problem of learning from both labeled source data and unlabeled target data. Existing methods focus on minoring the inter-domain gap b…

cs.CV2019★ 55 cited

Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification

Zhun Zhong, Liang Zheng, Zhiming Luo +2

This paper considers the domain adaptive person re-identification (re-ID) problem: learning a re-ID model from a labeled source domain and an unlabeled target domain. Conventional…

cs.CV2019

Joint Discriminative and Generative Learning for Person Re-identification

Zhedong Zheng, Xiaodong Yang, Zhiding Yu +3

Person re-identification (re-id) remains challenging due to significant intra-class variations across different cameras. Recently, there has been a growing interest in using genera…

cs.CV2019★ 24 cited

Significance-aware Information Bottleneck for Domain Adaptive Semantic Segmentation

Yawei Luo, Ping Liu, Tao Guan +2

For unsupervised domain adaptation problems, the strategy of aligning the two domains in latent feature space through adversarial learning has achieved much progress in image class…

cs.CV2019★ 101 cited

Contrastive Adaptation Network for Unsupervised Domain Adaptation

Guoliang Kang, Lu Jiang, Yi Yang +1

Unsupervised Domain Adaptation (UDA) makes predictions for the target domain data while manual annotations are only available in the source domain. Previous methods minimize the do…