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
arXiv paper abstract https://arxiv.org/abs/2112.10089v1
arXiv PDF paper https://arxiv.org/pdf/2112.10089v1.pdf
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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