Visible-infrared person re-identification (VI-ReID) is an important task in
night-time surveillance applications, since visible cameras are difficult to
capture valid appearance information under poor illumination conditions.
Compared to traditional person re-identification that handles only the
intra-modality discrepancy, VI-ReID suffers from additional cross-modality
discrepancy caused by different types of imaging systems. To reduce both intra-
and cross-modality discrepancies, we propose a Hierarchical Cross-Modality
Disentanglement (Hi-CMD) method, which automatically disentangles
ID-discriminative factors and ID-excluded factors from visible-thermal images.
We only use ID-discriminative factors for robust cross-modality matching
without ID-excluded factors such as pose or illumination. To implement our
approach, we introduce an ID-preserving person image generation network and a
hierarchical feature learning module. Our generation network learns the
disentangled representation by generating a new cross-modality image with
different poses and illuminations while preserving a person's identity. At the
same time, the feature learning module enables our model to explicitly extract
the common ID-discriminative characteristic between visible-infrared images.
Extensive experimental results demonstrate that our method outperforms the
state-of-the-art methods on two VI-ReID datasets. The source code is available
at: this https URL

本篇论文提出了一种基于 Hierarchical Cross-Modality Disentanglement 的方法，该方法使用 ID-discriminative factors 用于跨模式匹配，并使用 ID-preserving person image generation network 和 hierarchical feature learning module 生成一个新的跨模态图像来学习可见 - 红外图像的共同 ID 判别特征。