Image Re-Identification: Where Self-supervision Meets Vision-Language Learning
<|reference_start|>Image Re-Identification: Where Self-supervision Meets Vision-Language Learning: Recently, large-scale vision-language pre-trained models like CLIP have shown impressive performance in image re-identification (ReID). In this work, we explore whether self-supervision can aid in the use of CLIP for image ReID tasks. Specifically, we propose SVLL-ReID, the first attempt to integrate self-supervision and pre-trained CLIP via two training stages to facilitate the image ReID. We observe that: 1) incorporating language self-supervision in the first training stage can make the learnable text prompts more distinguishable, and 2) incorporating vision self-supervision in the second training stage can make the image features learned by the image encoder more discriminative. These observations imply that: 1) the text prompt learning in the first stage can benefit from the language self-supervision, and 2) the image feature learning in the second stage can benefit from the vision self-supervision. These benefits jointly facilitate the performance gain of the proposed SVLL-ReID. By conducting experiments on six image ReID benchmark datasets without any concrete text labels, we find that the proposed SVLL-ReID achieves the overall best performances compared with state-of-the-arts. Codes will be publicly available at https://github.com/BinWangGzhu/SVLL-ReID.<|reference_end|>
@article{wang2024image,
    title={Image Re-Identification: Where Self-supervision Meets Vision-Language
  Learning},
    author={Bin Wang, Yuying Liang, Lei Cai, Huakun Huang, and Huanqiang Zeng},
    journal={arXiv preprint arXiv:2407.20647},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.20647},
    primaryClass={cs.CV}
}