  Cross-modality person re-identification is a challenging problem which
retrieves a given pedestrian image in RGB modality among all the gallery images
in infrared modality. The task can address the limitation of RGB-based person
Re-ID in dark environments. Existing researches mainly focus on enlarging
inter-class differences of feature to solve the problem. However, few studies
investigate improving intra-class cross-modality similarity, which is important
for this issue. In this paper, we propose a novel loss function, called
Hetero-Center loss (HC loss) to reduce the intra-class cross-modality
variations. Specifically, HC loss can supervise the network learning the
cross-modality invariant information by constraining the intra-class center
distance between two heterogenous modalities. With the joint supervision of
Cross-Entropy (CE) loss and HC loss, the network is trained to achieve two
vital objectives, inter-class discrepancy and intra-class cross-modality
similarity as much as possible. Besides, we propose a simple and
high-performance network architecture to learn local feature representations
for cross-modality person re-identification, which can be a baseline for future
research. Extensive experiments indicate the effectiveness of the proposed
methods, which outperform state-of-the-art methods by a wide margin.
