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.

最近，视觉语言学习在增强基于视觉的人物再识别方面展示了巨大的潜力。本文提出了一种名为 Part-Informed Visual-language Learning (π-VL) 的方法，通过在 ReID 任务中利用部分信息语言监督来增强细粒度的视觉特征，该方法在四个常用的 ReID 基准测试中取得了显著的改进，特别是在最具挑战性的 MSMT17 数据库中达到了 90.3% 的 Rank-1 和 76.5% 的 mAP，而无需任何额外的技术手段。