对比三类行人重识别方法，发现语言对齐模型跨域表现更优。
Person Re-ID in 2025: Supervised, Self-Supervised, and Language-Aligned. What Works?
- 比较监督、自监督和语言对齐三种训练方式的性能差异。
- 11个模型在9个数据集上测试，语言对齐模型跨域准确率显著更高。
- 适合关注跨域泛化能力的研究者或工业应用开发者。
行人重识别（ReID）仍是计算机视觉中的挑战性问题。本文综述了多种训练范式，评估了当前最优ReID模型在跨域场景下的鲁棒性，并探讨了基础模型如何通过更丰富、可迁移的视觉表征提升泛化能力。我们对比了监督学习、自监督学习和语言对齐模型三类方法。研究旨在回答：监督模型能否在跨域场景中有效泛化？像SigLIP2这样的基础模型在ReID任务中表现如何？当前监督与基础模型在ReID中存在哪些缺陷？分析覆盖11个模型和9个数据集。结果表明：监督模型在其训练域内表现优异，但在跨域数据上性能急剧下降；而语言对齐模型虽未专门针对ReID训练，却展现出惊人的跨域鲁棒性。代码与数据已开源：https://github.com/moiiai-tech/object-reid-benchmark。
原文摘要 · Abstract (English)
Person Re-Identification (ReID) remains a challenging problem in computer vision. This work reviews various training paradigm and evaluates the robustness of state-of-the-art ReID models in cross-domain applications and examines the role of foundation models in improving generalization through richer, more transferable visual representations. We compare three training paradigms, supervised, self-supervised, and language-aligned models. Through the study the aim is to answer the following questions: Can supervised models generalize in cross-domain scenarios? How does foundation models like SigLIP2 perform for the ReID tasks? What are the weaknesses of current supervised and foundational models for ReID? We have conducted the analysis across 11 models and 9 datasets. Our results show a clear split: supervised models dominate their training domain but crumble on cross-domain data. Language-aligned models, however, show surprising robustness cross-domain for ReID tasks, even though they are not explicitly trained to do so. Code and data available at: https://github.com/moiiai-tech/object-reid-benchmark.
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