Rewarded Semi-Supervised Re-Identification on Identities Rarely Crossing Camera Views.

Abstract

Semi-supervised person re-identification (Re-ID) is an important approach for alleviating annotation costs when learning to match person images across camera views. Most existing works assume that training data contains abundant identities crossing camera views. However, this assumption is not true in many real-world applications, especially when images are captured in nonadjacent scenes for Re-ID in wider areas, where the identities rarely cross camera views. In this work, we operate semi-supervised Re-ID under a relaxed assumption of identities rarely crossing camera views, which is still largely ignored in existing methods. Since the identities rarely cross camera views, the underlying sample relations across camera views become much more uncertain, and deteriorate the noise accumulation problem in many advanced Re-ID methods that apply pseudo labeling for associating visually similar samples. To quantify such uncertainty, we parameterize the probabilistic relations between samples in a relation discovery objective for pseudo label training. Then, we introduce reward quantified by identification performance on a few labeled data to guide learning dynamic relations between samples for reducing uncertainty. Our strategy is called the Rewarded Relation Discovery (R 2 D), of which the rewarded learning paradigm is under-explored in existing pseudo labeling methods. To further reduce the uncertainty in sample relations, we perform multiple relation discovery objectives learning to discover probabilistic relations based on different prior knowledge of intra-camera affinity and cross-camera style variation, and fuse the complementary knowledge of different probabilistic relations by similarity distillation. To better evaluate semi-supervised Re-ID on identities rarely crossing camera views, we collect a new real-world dataset called REID-CBD, and perform simulation on benchmark datasets. Experiment results show that our method outperforms a wide range of semi-supervised and unsupervised learning methods.