Cloth-changing person re-identification (CC-ReID), which aims to match person identities under clothing changes, is a new rising research topic in recent years. However, typical biometrics-based CC-ReID methods often require cumbersome pose or body part estimators to learn cloth-irrelevant features from human biometric traits, which comes with high computational costs. Besides, the performance is significantly limited due to the resolution degradation of surveillance images. To address the above limitations, we propose an effective Identity-Sensitive Knowledge Propagation framework (DeSKPro) for CC-ReID. Specifically, a Cloth-irrelevant Spatial Attention module is introduced to eliminate the distraction of clothing appearance by acquiring knowledge from the human parsing module. To mitigate the resolution degradation issue and mine identity-sensitive cues from human faces, we propose to restore the missing facial details using prior facial knowledge, which is then propagated to a smaller network. After training, the extra computations for human parsing or face restoration are no longer required. Extensive experiments show that our framework outperforms state-ofthe-art methods by a large margin. Our code is available at https://github.com/KimbingNg/DeskPro.

Unsupervised domain adaptation (UDA) person re-identification (re-ID) aims to transfer knowledge learned from labeled source domain to unlabeled target domain and has been successfully applied into a wide range of real-world scenarios. However, existing methods are mainly ineffective at handling domain shift as well as being sensitive to camera styles due to the unannotated target domain. In this paper, we propose a Camera-style Separation and Uncertainty Estimation (CSUE) model to address the problem from two perspectives. To alleviate the negative effect of cross-camera variation, we introduce the Camera-aware Style Decoupling module to impose inter-and-intra camera constraints on the feature extracting stage. It can better mine and describe the latent camera invariant features. Moreover, to avoid the inherent defect of clustering, an Uncertainty Modeling module is constructed via estimating the certainty, which helps progressively refine the pseudo labels. Extensive experiments on widely used datasets demonstrate the state-of-the-art performance of our model under the UDA re-ID setting.

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