实现任意粒度的基于文本的行人检索
Achieving Text-based Person Retrieval with Any Granularity
- National Key Laboratory of Multispectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院多光谱信息智能处理技术国家重点实验室）
- School of Software Engineering, Huazhong University of Science and Technology（华中科技大学软件工程学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究针对基于文本的行人检索中查询粒度不确定问题提出新范式，构建多粒度数据集和评估基准，提出CMAM框架，通过多种策略实现粒度感知检索，实验证明其性能优于现有方法，为行人检索系统奠定基础。
AI中文摘要：
基于文本的行人检索面临一个关键但未充分探索的挑战：现实场景中查询粒度的固有不确定性。本文引入了一种新范式——任意粒度的基于文本的行人检索，并提供了系统解决方案。首先，形式化了五级粒度谱并构建UFine6926 - MG数据集。其次，提出MG - Eval评估基准。然后，提出CMAM框架，通过正交专家感知、概率对齐和粒度一致推理实现粒度感知检索。实验表明CMAM在各粒度级别均显著优于现有方法，为更实用的行人检索系统奠定了基础。
英文摘要：
Text-based person retrieval faces a critical but under-explored challenge: the inherent uncertainty of query granularity in real-world scenarios. This paper introduces a new paradigm, Text-based Person Retrieval with Any Granularity, and provides a systematic solution. First, we formalize a five-level granularity spectrum and construct UFine6926-MG, a high-quality multi-grained dataset annotated comprehensively at all granularities via a novel Multi-grained Text Annotation Engine. Second, acknowledging that coarse queries naturally correspond to multiple valid candidates, we propose MG-Eval, a holistic evaluation benchmark with progressively detailed texts and cross-identity labels that reflect real-world semantics, alongside tailored evaluation metrics and protocols. Third, after a comprehensive diagnosis reveals the systemic limitations of existing research, we propose the Cross-modal Multi-grained Aligning and Matching (CMAM) framework. CMAM achieves granularity-aware retrieval through: 1) orthogonal-expert perception to disentangle granularity-specific features; 2) probabilistic alignment to model many-to-many matches under query uncertainty; and 3) granularity-consistent reasoning to steer feature learning via joint cross-modal granularity verification. Experiments demonstrate that CMAM significantly outperforms state-of-the-art methods across all granularity levels. This work establishes a foundational benchmark and a robust baseline, paving the way for more practical person retrieval systems.
