MvHAAN: multi-view hierarchical attention adversarial network for person re-identification

Abstract

Person re-identification (re-id) aims to recognize pedestrians across different camera views, which enjoys popularity in computer vision area recently. Notwithstanding the progress achieved by existing methods in rising matching rate, the prevailing solutions still suffer from two impossible-to-ignore issues: (I) multi-grained view-consistent discriminative feature learning across multiple views has barely been explored, and (II) latent non-linear correlation between multiple views is insufficient captured. To this end, this article proposes a novel end-to-end unsupervised framework for person re-id task, dubbed Multi-view Hierarchical Attention Adversarial Network (MvHAAN). This framework enjoys two merits: First, a hierarchical attention mechanism in multi-view networks is present to learn multi-grained view-consistent discriminative features; Second, a multi-view adversarial correlation learning strategy is involved to excavate complex non-linear correlation from all views simultaneously. To the best of our knowledge, it is the early attempt of marrying multi-view deep correlation learning with adversarial learning to further reduce multi-view heterogeneity. Extensive evaluations on three person re-id benchmark datasets verify that the proposed method delivers superior performance of unsupervised person re-id.