Contrastive Multiple Instance Learning for Weakly Supervised Person ReID
<|reference_start|>Contrastive Multiple Instance Learning for Weakly Supervised Person ReID: 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.<|reference_end|>
@article{tyo2024contrastive,
    title={Contrastive Multiple Instance Learning for Weakly Supervised Person ReID},
    author={Jacob Tyo and Zachary C. Lipton},
    journal={arXiv preprint arXiv:2402.07685},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.07685},
    primaryClass={cs.CV cs.LG}
}