Reference-oriented Loss for Person Re-identification

Deep metric learning methods are quite effective in exploring discriminative feature embeddings, among which triplet loss and its variants are widely utilized. However, in existing methods, the tightness information for intra-class samples is ignored, leading to large intra-class divergence and severe inter-class overlapping problem. To address this issue, a novel loss function called reference-oriented triplet loss is proposed in this paper. The proposed method introduces several reference images to guide training. More specifically, distances between the reference image and images of the same identity are required to be as similar as possible. By introducing reference images, images from the same class become much closer with each other and the inter-class overlapping problem is alleviated. Comparing to baseline batch hard triplet loss, the mAP accuracy increases by 3.75%/5.69% on person re-ID datasets Market1501 and DukeMTMC-Reid. Comparison results with state-of-the-art algorithms also demonstrate effectiveness of the proposed algorithm.