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Reidentify people in new scenes better by accounting for camera styles with CA-UReID

Reidentify people in new scenes better by accounting for camera styles with CA-UReID


Camera-aware Style Separation and Contrastive Learning for Unsupervised Person Re-identification



Unsupervised person re-identification (ReID) is a challenging task without data annotation to guide discriminative learning.


Existing methods attempt to solve this problem by clustering extracted embeddings to generate pseudo labels.


However, most methods ignore the intra-class gap caused by camera style variance


... propose a camera-aware style separation and contrastive learning method (CA-UReID), which directly separates camera styles in the feature space


It can explicitly divide the learnable feature into camera-specific and camera-agnostic parts, reducing the influence of different cameras.


... demonstrate the superiority of our method over the state-of-the-art methods on the unsupervised person ReID task.



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