  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.
