  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.
