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Part-Aligned Bilinear Representations for Person Re-identification

2018

Article

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We propose a novel network that learns a part-aligned representation for person re-identification. It handles the body part misalignment problem, that is, body parts are misaligned across human detections due to pose/viewpoint change and unreliable detection. Our model consists of a two-stream network (one stream for appearance map extraction and the other one for body part map extraction) and a bilinear-pooling layer that generates and spatially pools a part- aligned map. Each local feature of the part-aligned map is obtained by a bilinear mapping of the corresponding local appearance and body part descriptors. Our new representation leads to a robust image matching similarity, which is equiv- alent to an aggregation of the local similarities of the corresponding body parts combined with the weighted appearance similarity. This part-aligned representa- tion reduces the part misalignment problem significantly. Our approach is also advantageous over other pose-guided representations (e.g., extracting represen- tations over the bounding box of each body part) by learning part descriptors optimal for person re-identification. For training the network, our approach does not require any part annotation on the person re-identification dataset. Instead, we simply initialize the part sub-stream using a pre-trained sub-network of an existing pose estimation network, and train the whole network to minimize the re-identification loss. We validate the effectiveness of our approach by demon- strating its superiority over the state-of-the-art methods on the standard bench- mark datasets, including Market-1501, CUHK03, CUHK01 and DukeMTMC, and standard video dataset MARS.

Author(s): Yumin Suh and Jingdong Wang and Siyu Tang and Tao Mei and Kyoung Mu Lee
Journal: arXiv preprint arXiv:1804.07094
Year: 2018

Department(s): Perceiving Systems
Bibtex Type: Article (article)
Paper Type: Journal

Institution: arXiv
URL: https://arxiv.org/abs/1804.07094
Attachments: Part-AlignedBilinearRepresentationsforPersonRe-identification

BibTex

@article{Person Re-identification,
  title = {Part-Aligned Bilinear Representations for Person Re-identification},
  author = {Suh, Yumin and Wang, Jingdong and Tang, Siyu and Mei, Tao and Lee, Kyoung Mu},
  journal = {arXiv preprint arXiv:1804.07094},
  institution = {arXiv},
  year = {2018},
  url = {https://arxiv.org/abs/1804.07094}
}