2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IVASR-3.3
Paper Title UNSUPERVISED PERSON RE-IDENTIFICATION VIA NEAREST NEIGHBOR COLLABORATIVE TRAINING STRATEGY
Authors Qing Tang, Kang-Hyun Jo, University of Ulsan, Republic of Korea
SessionMLR-APPL-IVASR-3: Machine learning for image and video analysis, synthesis, and retrieval 3
LocationArea E
Session Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image & video analysis, synthesis, and retrieval
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Because of the lack of human-labeled data, the challenge of unsupervised person re-identification (re-ID) is to learn to generate correct pseudo labels for training. Unlike the human-labeled annotation, the generated pseudo labels contain the noise labels that harm the model's performance. In this paper, we propose the Nearest Neighbors Collaborative Training (NNCT) strategy to mitigate the effects of noisy labels by utilizing information of the nearest neighbor of an image. The proposed NNCT trains the image and its nearest neighbor collaboratively, thereby enhancing the generalization capability of the network and shortening the distance with neighbors. To make training using the up-to-date nearest neighbor possible, we introduce a Pseudo Label Memory Bank (PLMB) to store the up-to-date labels of all images. The experimental results confirm the superiority of the proposed method, which surpasses state-of-the-arts on two mainstream person re-ID datasets, Market-1501, and DukeMTMC-reID in both fully unsupervised learning manner and Unsupervised Domain Adaptation (UDA) manner.