• Corpus ID: 232352862

Disentanglement-based Cross-Domain Feature Augmentation for Effective Unsupervised Domain Adaptive Person Re-identification

@article{Zhang2021DisentanglementbasedCF,
  title={Disentanglement-based Cross-Domain Feature Augmentation for Effective Unsupervised Domain Adaptive Person Re-identification},
  author={Zhizheng Zhang and Cuiling Lan and Wenjun Zeng and Quanzeng You and Zicheng Liu and Kecheng Zheng and Zhibo Chen},
  journal={ArXiv},
  year={2021},
  volume={abs/2103.13917}
}
Unsupervised domain adaptive (UDA) person reidentification (ReID) aims to transfer the knowledge from the labeled source domain to the unlabeled target domain for person matching. One challenge is how to generate target domain samples with reliable labels for training. To address this problem, we propose a Disentanglement-based Cross-Domain Feature Augmentation (DCDFA) strategy, where the augmented features characterize well the target and source domain data distributions while inheriting… 

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