Semantically Self-Aligned Network for Text-to-Image Part-aware Person Re-identification

• URL: http://arxiv.org/abs/2107.12666v1
• Date: Tue, 27 Jul 2021 08:26:47 GMT
• Title: Semantically Self-Aligned Network for Text-to-Image Part-aware Person Re-identification
• Authors: Zefeng Ding, Changxing Ding, Zhiyin Shao, Dacheng Tao
• Abstract summary: Text-to-image person re-identification (ReID) aims to search for images containing a person of interest using textual descriptions. We propose a Semantically Self-Aligned Network (SSAN) to handle the above problems. To expedite future research in text-to-image ReID, we build a new database named ICFG-PEDES.
• Score: 78.45528514468836
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-to-image person re-identification (ReID) aims to search for images containing a person of interest using textual descriptions. However, due to the significant modality gap and the large intra-class variance in textual descriptions, text-to-image ReID remains a challenging problem. Accordingly, in this paper, we propose a Semantically Self-Aligned Network (SSAN) to handle the above problems. First, we propose a novel method that automatically extracts semantically aligned part-level features from the two modalities. Second, we design a multi-view non-local network that captures the relationships between body parts, thereby establishing better correspondences between body parts and noun phrases. Third, we introduce a Compound Ranking (CR) loss that makes use of textual descriptions for other images of the same identity to provide extra supervision, thereby effectively reducing the intra-class variance in textual features. Finally, to expedite future research in text-to-image ReID, we build a new database named ICFG-PEDES. Extensive experiments demonstrate that SSAN outperforms state-of-the-art approaches by significant margins. Both the new ICFG-PEDES database and the SSAN code are available at https://github.com/zifyloo/SSAN.

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