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Although unsupervised person re-identification (RE-ID) has drawn increasingresearch attentions due to its potential to address the scalability problem ofsupervised RE-ID models, it is very challenging to learn discriminativeinformation in the absence of pairwise labels across disjoint camera views. Toovercome this problem, we propose a deep model for the soft multilabel learningfor unsupervised RE-ID. The idea is to learn a soft multilabel (real-valuedlabel likelihood vector) for each unlabeled person by comparing (andrepresenting) the unlabeled person with a set of known reference persons froman auxiliary domain. We propose the soft multilabel-guided hard negative miningto learn a discriminative embedding for the unlabeled target domain byexploring the similarity consistency of the visual features and the softmultilabels of unlabeled target pairs. Since most target pairs are cross-viewpairs, we develop the cross-view consistent soft multilabel learning to achievethe learning goal that the soft multilabels are consistently good acrossdifferent camera views. To enable effecient soft multilabel learning, weintroduce the reference agent learning to represent each reference person by areference agent in a joint embedding. We evaluate our unified deep model onMarket-1501 and DukeMTMC-reID. Our model outperforms the state-of-the-artunsupervised RE-ID methods by clear margins. Code is available athttps://github.com/KovenYu/MAR.

Unsupervised Person Re-identification by Soft Multilabel Learning