Self-Supervised Recovery and Guide for Low-Resolution Person Re-Identification

Low-resolution person re-identification is a challenging task to match low-resolution (LR) probes with high-resolution (HR) gallery images. To address the resolution gap, existing methods typically recover missing details for LR probes by super-resolution, and then match the recovered HR images (instead of the original LR probes) with gallery images. However, they usually pre-specify fixed scale factors for all LR images, and ignore that choosing a preferable scale factor for each image can recover more discriminative content and accordingly benefit the re-id performance. Moreover, these methods do not focus on learning LR representations themselves and always resort to extra recovery to handle LR probes, which is quite time-consuming during inference. To tackle these problems, we propose a Self-supervised Recovery and Guide (SRG) re-id model in this paper. Given LR images during training, our model first recovers more discriminative HR images by finding out preferable scale factors, and further leverages them as guide to improve original LR representations. By enforcing LR representations to approach the self-recovered HR guide in a self-supervised manner, our model can learn more discriminative representations for LR images. As a result, our model is able to directly handle LR probes without requiring recovery during inference, thereby reducing inference time significantly. Extensive experiments demonstrate the effectiveness of our method on four datasets.