VMRFANet:View-Specific Multi-Receptive Field Attention Network for Person Re-identification

• URL: http://arxiv.org/abs/2001.07354v1
• Date: Tue, 21 Jan 2020 06:31:18 GMT
• Title: VMRFANet:View-Specific Multi-Receptive Field Attention Network for Person Re-identification
• Authors: Honglong Cai, Yuedong Fang, Zhiguan Wang, Tingchun Yeh, Jinxing Cheng
• Abstract summary: We propose a novel multi-receptive field attention (MRFA) module that utilizes filters of various sizes to help network focusing on informative pixels. We present a view-specific mechanism that guides attention module to handle the variation of view conditions. Our method achieves 95.5% / 88.1% in rank-1 / mAP on Market-1501, 88.9% / 80.0% on DukeMTMC-reID, 81.1% / 78.8% on CUHK03 labeled dataset and 78.9% / 75.3% on CUHK03 detected dataset.
• Score: 3.1498833540989413
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Person re-identification (re-ID) aims to retrieve the same person across different cameras. In practice, it still remains a challenging task due to background clutter, variations on body poses and view conditions, inaccurate bounding box detection, etc. To tackle these issues, in this paper, we propose a novel multi-receptive field attention (MRFA) module that utilizes filters of various sizes to help network focusing on informative pixels. Besides, we present a view-specific mechanism that guides attention module to handle the variation of view conditions. Moreover, we introduce a Gaussian horizontal random cropping/padding method which further improves the robustness of our proposed network. Comprehensive experiments demonstrate the effectiveness of each component. Our method achieves 95.5% / 88.1% in rank-1 / mAP on Market-1501, 88.9% / 80.0% on DukeMTMC-reID, 81.1% / 78.8% on CUHK03 labeled dataset and 78.9% / 75.3% on CUHK03 detected dataset, outperforming current state-of-the-art methods.

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