  Recently, visual-language learning has shown great potential in enhancing
visual-based person re-identification (ReID). Existing visual-language
learning-based ReID methods often focus on whole-body scale image-text feature
alignment, while neglecting supervisions on fine-grained part features. This
choice simplifies the learning process but cannot guarantee within-part feature
semantic consistency thus hindering the final performance. Therefore, we
propose to enhance fine-grained visual features with part-informed language
supervision for ReID tasks. The proposed method, named Part-Informed
Visual-language Learning ($\pi$-VL), suggests that (i) a human parsing-guided
prompt tuning strategy and (ii) a hierarchical fusion-based visual-language
alignment paradigm play essential roles in ensuring within-part feature
semantic consistency. Specifically, we combine both identity labels and parsing
maps to constitute pixel-level text prompts and fuse multi-stage visual
features with a light-weight auxiliary head to perform fine-grained image-text
alignment. As a plug-and-play and inference-free solution, our $\pi$-VL
achieves substantial improvements over previous state-of-the-arts on four
common-used ReID benchmarks, especially reporting 90.3% Rank-1 and 76.5% mAP
for the most challenging MSMT17 database without bells and whistles.
