Multi-Stream Refining Network for Person Re-Identification

Abstract

Viewpoint change, pose variation and background clutter have adverse impacts on similarity evaluation for person re-identification. Because of its distinction and reliability, person saliency has been applied to model person appearance characteristics. However, such valuable information is not fully exploited to compute similarities of person images with existing deep methods. To this end, we present a novel multi-stream refining based deep multi-task learning scheme that aggregates multi-stage salient embedding features in the network to boost the retrieval performance. Specifically, the backbone network is divided into four stages and a channel significance self-learning sub-module is introduced to strengthen the importance of saliency channels adaptively. Meanwhile, an enhancement sub-module is employed to extract the common information and different information from the channels. Finally, a multi-stream multi-task learning framework combining four-stage branches is adopted to learn discriminative features. Compared with the state-of-the-art approaches, our model achieves competitive performance on three publicly available datasets, i.e., Market-1501, MSMT17, and CUHK03. The experimental results demonstrate the superiority of our method, which achieves 95.67%/88.51%, 87.53%/65.54%, and 89.32%/78.99% on Rank-1/mAP, respectively.

Keywords

• Salient channels
• refining module
• multi-stream
• multi-task