Prediction and Recovery for Adaptive Low-Resolution Person Re-Identification

Abstract

Low-resolution person re-identification (LR re-id) is a challenging task with low-resolution probes and high-resolution gallery images. To address the resolution mismatch, existing methods typically recover missing details for low-resolution probes by super-resolution. However, they usually pre-specify fixed scale factors for all images, and ignore the fact that choosing a preferable scale factor for certain image content probably greatly benefits the identification. In this paper, we propose a novel Prediction, Recovery and Identification (PRI) model for LR re-id, which adaptively recovers missing details by predicting a preferable scale factor based on the image content. To deal with the lack of ground-truth optimal scale factors, our model contains a self-supervised scale factor metric that automatically generates dynamic soft labels. The generated labels indicate probabilities that each scale factor is optimal, which are used as guidance to enhance the content-aware scale factor prediction. Consequently, our model can more accurately predict and recover the content-aware details, and achieve state-of-the-art performances on four LR re-id datasets.