Improving Person Re-identification by Multi-Task Learning

Abstract For person re-identification, the core task is to find effective representations of a person image. As Multi-Task Learning can achieve great performance in seeking robust features, we propose a novel Multi-Task Learning Network (MTNet) with four different losses for person re-identification (re-ID). Our MTNet is an end-to-end deep learning framework, which all the parameters and losses can be jointly optimized. In our method we combine two tasks closely corresponding to person re-identification, pedestrian identity task and pedestrian attribute task, who provide complementary information from different perspective by integrating multi-context informations. Attribute focuses on some special aspects of a person, while identity pays more attention to overall contour and appearance. Meanwhile, both classification and verification losses are employed to optimize the distance of samples. Identification losses are used to construct a large class space, while verification losses are applied optimize the space by minimizing the distance between similar images and maximizing the distance between dissimilar images. In the experiments, our MTNet achieves the state-of-the-art results on two typical datasets Market1501 [1] and DukeMTMC-reID [2].