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-4.3
Paper Title LEARNING GENERIC FEATURE REPRESENTATIONS WITH ADVERSARIAL REGULARIZATION FOR PERSON RE-IDENTIFICATION
Authors Qindong Zhang, Sanping Zhou, Jinjun Wang, Xi'an Jiaotong University, 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 Many existing person re-identification (Re-ID) methods can achieve human-level accuracy on a single dataset, while most of them can be poorly generalized to other datasets. This is mainly caused by different data distributions between different domains. In this paper, we propose a novel adversarial regularization method to address this issue. Specifically, the features extracted from different datasets will be constrained and focused to follow a more similar distribution during the training process. As a result, our method can learn a feature representation with better inter-domain invariance, which will improve the generalization ability of the resulting model. Besides, our method is flexible and can be combined with any feature learning network. Extensive experiments on both Market1501 and DukeMTMC-reID datasets have demonstrated the effectiveness of our method.