MRLReID: Unconstrained Cross-Resolution Person Re-Identification With Multi-Task Resolution Learning

Cross-resolution person re-identification (ReID) is a challenging task that addresses the issue of matching individuals across different resolution conditions. Traditional person ReID methods often assume that images have sufficiently high resolution and overlook the practical scenarios involving low-resolution or blurry images. Existing cross-resolution ReID approaches either utilize image super-resolution techniques to improve the quality of low-resolution images or extract and learn resolution invariant features for person representation. Although multi-task learning has been applied in ReID to integrate auxiliary tasks including attribute recognition, image super-resolution, and so on, how to incorporate the vital resolution learning task into cross-resolution ReID has rarely explored before. Therefore, we propose a novel multi-task resolution learning based ReID network named MRLReID. Our approach treats ross-resolution person ReID as the primary task and the resolution estimation as an auxiliary task. Our network simultaneously learns the resolution information and person identity information of images, aiming to improve cross-resolution person ReID performance. Considering that existing similuated cross-resolution datasets are too simple to mimic unconstrained scenario, we further employ image degradation technique to simulate more realistic cross-resolution ReID datasets. We evaluate our method on two real-world cross-resolution datasets and two newly simulated cross-resolution datasets, and both intra-dataset and cross-dataset evaluations demonstrate the effectiveness and superiority of our method in cross-resolution person ReID. The codes and datasets are available at https://github.com/amateurbo/MRLReID.