Homogeneous-to-Heterogeneous: Unsupervised Learning for RGB-Infrared Person Re-Identification

Abstract

RGB-Infrared (RGB-IR) cross-modality person re-identification (re-ID) is attracting more and more attention due to requirements for 24-h scene surveillance. However, the high cost of labeling person identities of an RGB-IR dataset largely limits the scalability of supervised models in real-world scenarios. In this paper, we study the unsupervised RGB-IR person re-ID problem (or briefly uRGB-IR re-ID) in which no identity annotations are available in RGB-IR cross-modality datasets. Considering that intra-modality (i.e., RGB-RGB or IR-IR) re-ID is much easier than cross-modality re-ID and can provide shared knowledge for RGB-IR re-ID, we propose a two-stage method to solve the uRGB-IR re-ID, namely homogeneous-to-heterogeneous learning. In the first stage, the unsupervised self-learning method is conducted to learn the intra-modality feature representation and to generate the pseudo-labeled identities of person images separately for each modality. In the second stage, heterogeneous learning is used to learn a shared discriminative feature representation by distilling the knowledge from intra-modality pseudo-labels, to align two modalities via a modality-based consistent learning module, and finally to target modality-invariant learning via a pseudo-labeled positive instance selection module. With the use of homogeneous-to-heterogeneous learning, the proposed unsupervised framework greatly reduces the modality gap and thus learns a robust feature representation against RGB and infrared modalities, leading to promising accuracy. We also propose a novel cross-modality re-ranking approach that includes a self-modality search and a cycle-modality search to tailor the uRGB-IR re-ID. Unlike conventional re-ranking, the proposed re-ranking method takes a modality-based constraint into re-ranking and thus can select more reliable nearest neighbors, which greatly improves uRGB-IR re-ID. The experimental results demonstrate the superiority of our approach on the SYSU-MM01 and RegDB datasets.