Title: ReText: Text Boosts Generalization in Image-Based Person Re-identification
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: person re-identification, generalizable person re-identification, multimodal learning, metric learning

Abstract: Generalizable image-based person re-identification (Re-ID) aims to recognize individuals across cameras in unseen domains without retraining. While multiple existing approaches address the domain gap through complex architectures, recent findings indicate that better generalization can be achieved by stylistically diverse single-camera data. Although this data is easy to collect, it lacks complexity due to minimal cross-view variation. We propose ReText, a novel method trained on a mixture of multi-camera Re-ID data and single-camera data, where the latter is complemented by textual descriptions to enrich semantic cues. During training, ReText jointly optimizes three tasks: (1) Re-ID on multi-camera data, (2) image-text matching, and (3) image reconstruction guided by text on single-camera data. Experiments demonstrate that ReText achieves strong generalization and significantly outperforms state-of-the-art methods on cross-domain Re-ID benchmarks. To the best of our knowledge, this is the first work to explore multimodal joint learning on a mixture of multi-camera and single-camera data in image-based person Re-ID. Code will be made publicly available.

TL;DR: We show that properly leveraging text significantly boosts generalizable image-based person Re-ID. Our ReText sets a new state-of-the-art on cross-domain benchmarks.