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 IDARS-4.4
Paper Title UNSUPERVISED PERSON RE-IDENTIFICATION VIA GLOBAL-LEVEL AND PATCH-LEVEL DISCRIMINATIVE FEATURE LEARNING
Authors Zongzhe Sun, Feng Zhao, Feng Wu, University of Science and Technology of China, China
SessionARS-4: Re-Identification and Retrieval
LocationArea I
Session Time:Wednesday, 22 September, 08:00 - 09:30
Presentation Time:Wednesday, 22 September, 08:00 - 09:30
Presentation Poster
Topic Image and Video Analysis, Synthesis, and Retrieval: Image & Video Storage and Retrieval
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Due to the lack of labeled data, it is usually difficult for an unsupervised person re-identification (re-ID) model to learn discriminative features. To address this issue, we propose a global-level and patch-level unsupervised feature learning framework that utilizes both global and local information to obtain more discriminative features. For global-level learning, we design a global similarity-based loss (GSL) to leverage the similarities between whole images. Along with a memory-based non-parametric classifier, the GSL pulls credible samples closer to help train a discriminative model. For patch-level learning, we use a patch generation module to produce different patches. Applying the patch-based discriminative feature learning loss and image-level feature learning loss, the patch branch in the network can learn better representative patch features. Combining the global-level learning with patch-level learning, we obtain a more distinguishable re-ID model. Experimental results obtained on Market-1501 and DukeMTMC-reID datasets validate that our method has great superiority and effectiveness in unsupervised person re-ID.