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Achieving Text-based Person Retrieval with Any Granularity

arXiv cs.CV Multimodal & Generative Jialong Zuo, Hanyu Zhou, Dongyue Wu, Yongtai Deng, Mengdan Tan, Nong Sang, Changxin Gao, Xiang Bai 2026-07-23

TL;DR - This paper introduces a benchmark, dataset, and framework for text-based person retrieval across five query-granularity levels. It matters because coarse descriptions can validly match multiple people, requiring uncertainty-aware alignment and evaluation.

UFine6926-MG provides multi-grained annotations spanning all five granularity levels.
MG-Eval uses progressively detailed descriptions, cross-identity labels, and tailored metrics.
CMAM combines orthogonal experts, probabilistic many-to-many alignment, and granularity-consistent reasoning.
Experiments report state-of-the-art performance across every evaluated granularity level.

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