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Learning Disentangled Representation Implicitly via Transformer for Occluded Person Re-Identification
[article]
2021
arXiv
pre-print
Person re-identification (re-ID) under various occlusions has been a long-standing challenge as person images with different types of occlusions often suffer from misalignment in image matching and ranking. Most existing methods tackle this challenge by aligning spatial features of body parts according to external semantic cues or feature similarities but this alignment approach is complicated and sensitive to noises. We design DRL-Net, a disentangled representation learning network that
arXiv:2107.02380v1
fatcat:kfjfp6zchzeq7c53udq6wzfncu