The acquisition of large-scale, precisely labeled datasets for person re-identification (ReID) poses a significant challenge. Weakly supervised ReID has begun to address this issue, although its performance lags behind fully supervised methods. In response, we introduce Contrastive Multiple Instance Learning (CMIL), a novel framework tailored for more effective weakly supervised ReID. CMIL distinguishes itself by requiring only a single model and no pseudo labels while leveraging contrastive losses -- a technique that has significantly enhanced traditional ReID performance yet is absent in all prior MIL-based approaches. Through extensive experiments and analysis across three datasets, CMIL not only matches state-of-the-art performance on the large-scale SYSU-30k dataset with fewer assumptions but also consistently outperforms all baselines on the WL-market1501 and Weakly Labeled MUddy racer re-iDentification dataset (WL-MUDD) datasets. We introduce and release the WL-MUDD dataset, an extension of the MUDD dataset featuring naturally occurring weak labels from the real-world application at PerformancePhoto.co. All our code and data are accessible at https://drive.google.com/file/d/1rjMbWB6m-apHF3Wg_cfqc8QqKgQ21AsT/view?usp=drive_link.

翻译：行人重识别（ReID）中大规模、精确标注数据集的获取面临重大挑战。弱监督ReID已开始解决这一问题，但其性能仍落后于全监督方法。为此，我们提出对比式多实例学习（CMIL），这是一种专为更有效的弱监督ReID设计的新型框架。CMIL的独特之处在于仅需单一模型且无需伪标签，同时利用对比损失——该技术已显著提升传统ReID性能，但在所有先前基于多实例学习（MIL）的方法中均未被采用。通过在三个数据集上的广泛实验与分析，CMIL不仅在更大规模SYSU-30k数据集上以更少假设达到最先进性能，还在WL-market1501和弱标注MUDD行人重识别数据集（WL-MUDD）上持续优于所有基线方法。我们引入并公开了WL-MUDD数据集，该数据集是MUDD数据集的扩展，包含来自PerformancePhoto.co实际应用中自然产生的弱标签。所有代码与数据均可在https://drive.google.com/file/d/1rjMbWB6m-apHF3Wg_cfqc8QqKgQ21AsT/view?usp=drive_link获取。