  To tackle the challenge of vehicle re-identification (Re-ID) in complex
lighting environments and diverse scenes, multi-spectral sources like visible
and infrared information are taken into consideration due to their excellent
complementary advantages.
  However, multi-spectral vehicle Re-ID suffers cross-modality discrepancy
caused by heterogeneous properties of different modalities as well as a big
challenge of the diverse appearance with different views in each identity.
  Meanwhile, diverse environmental interference leads to heavy sample
distributional discrepancy in each modality.
  In this work, we propose a novel cross-directional consistency network to
simultaneously overcome the discrepancies from both modality and sample
aspects.
  In particular, we design a new cross-directional center loss to pull the
modality centers of each identity close to mitigate cross-modality discrepancy,
while the sample centers of each identity close to alleviate the sample
discrepancy. Such strategy can generate discriminative multi-spectral feature
representations for vehicle Re-ID.
  In addition, we design an adaptive layer normalization unit to dynamically
adjust individual feature distribution to handle distributional discrepancy of
intra-modality features for robust learning.
  To provide a comprehensive evaluation platform, we create a high-quality
RGB-NIR-TIR multi-spectral vehicle Re-ID benchmark (MSVR310), including 310
different vehicles from a broad range of viewpoints, time spans and
environmental complexities.
  Comprehensive experiments on both created and public datasets demonstrate the
effectiveness of the proposed approach comparing to the state-of-the-art
methods.
