Dual Refinement: Joint Label and Feature Refinement for Unsupervised Domain Adaptive Person Re Identification
Unsupervised domain adaptive (UDA) person re identification (re ID) is a challenging task due to the missing of labels for the target domain data. To handle this problem, some recent works adopt clustering algorithms to off line generate pseudo labels, which can then be used as the supervision signal for on line feature learning in the target domain. However, the off line generated labels often contain lots of noise that significantly hinders the discriminability of the on line learned features, and thus limits the final UDA re ID performance. To this end, we propose a novel approach, called Dual Refinement, that jointly refines pseudo labels at the off line clustering phase and features at the on line training phase, to alternatively boost the label purity and feature discriminability in the target domain for more reliable re ID. Specifically, at the off line phase, a new hierarchical clustering scheme is proposed, which selects representative prototypes for every coarse cluster. Thus, labels can be effectively refined by using the inherent hierarchical information of person images. Besides, at the on line phase, we propose an instant memory spread out (IM spread out) regularization, that takes advantage of the proposed instant memory bank to store sample features of the entire dataset and enable spread out feature learning over the entire training data instantly. Our Dual Refinement method reduces the influence of noisy labels and refines the learned features within the alternative training process. Experiments demonstrate that our method outperforms the state of the art methods by a large margin.
IEEE Transactions on Image Processing