Exploring Part-Informed Visual-Language Learning for Person Re-Identification

• URL: http://arxiv.org/abs/2308.02738v2
• Date: Fri, 21 Mar 2025 10:42:26 GMT
• Title: Exploring Part-Informed Visual-Language Learning for Person Re-Identification
• Authors: Yin Lin, Yehansen Chen, Baocai Yin, Jinshui Hu, Bing Yin, Cong Liu, Zengfu Wang,
• Abstract summary: We propose Part-Informed Visual-language Learning ($pi$-VL) to enhance fine-grained visual features with part-informed language supervisions for ReID tasks.<n>$pi$-VL introduces a human parsing-guided prompt tuning strategy and a hierarchical visual-language alignment paradigm to ensure within-part feature semantic consistency.<n>As a plug-and-play and inference-free solution, our $pi$-VL achieves performance comparable to or better than state-of-the-art methods on four commonly used ReID benchmarks.
• Score: 52.92511980835272
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recently, visual-language learning (VLL) has shown great potential in enhancing visual-based person re-identification (ReID). Existing VLL-based ReID methods typically focus on image-text feature alignment at the whole-body level, while neglecting supervision on fine-grained part features, thus lacking constraints for local feature semantic consistency. To this end, we propose Part-Informed Visual-language Learning ($\pi$-VL) to enhance fine-grained visual features with part-informed language supervisions for ReID tasks. Specifically, $\pi$-VL introduces a human parsing-guided prompt tuning strategy and a hierarchical visual-language alignment paradigm to ensure within-part feature semantic consistency. The former combines both identity labels and human parsing maps to constitute pixel-level text prompts, and the latter fuses multi-scale 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 performance comparable to or better than state-of-the-art methods on four commonly used ReID benchmarks. Notably, it reports 91.0% Rank-1 and 76.9% mAP on the challenging MSMT17 database, without bells and whistles.

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