Progressive sample mining and representation learning for one-shot person re-identification

Highlights：

• We propose a pseudo label sampling mechanism for one-shot Re-ID task, which is based on the relative sample distance to the feature center of each labelled sample. Our sampling mechanism ensures the feasibility of forming a positive pair and a negative pair samples for each class label, which paves the way for the utilization of the HSoften-Triplet-Loss.

• We achieve the state-of-the-art mAP score of 42.7% on Market1501 and 40.3% on DukeMTMC-Reid, 16.5 and 11.8 higher than EUG respectively.

摘要

•We identify the necessity of triplet loss in image based one-shot Re-ID, where the use of noisy pseudo labels for training is inevitable. Considering the nature of pseudo labels, we introduce an HSoften-Triplet-Loss to soften the negative influence of incorrect pseudo label. Meanwhile, a new batch formation rule is designed by taking different nature of labelled samples and pseudo labelled samples into account.•We propose a pseudo label sampling mechanism for one-shot Re-ID task, which is based on the relative sample distance to the feature center of each labelled sample. Our sampling mechanism ensures the feasibility of forming a positive pair and a negative pair samples for each class label, which paves the way for the utilization of the HSoften-Triplet-Loss.•We achieve the state-of-the-art mAP score of 42.7% on Market1501 and 40.3% on DukeMTMC-Reid, 16.5 and 11.8 higher than EUG respectively.