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Achieving Text-based Person Retrieval with Any Granularity

Research Multimodal & Generative

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Merged summary

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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Achieving Text-based Person Retrieval with Any Granularity

arXiv cs.CV Jialong Zuo, Hanyu Zhou, Dongyue Wu, Yongtai Deng, Mengdan Tan, Nong Sang, Changxin Gao, Xiang Bai 2026-07-23 arXiv:2607.21057 doi:10.1109/tpami.2026.3708762
Public signals OpenAlex citations 0
Providers: Hugging Face · N/A OpenAlex · Citations 0 Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-08-24 14:35:27.153650 UTC

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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