  Learning modality invariant features is central to the problem of
Visible-Thermal cross-modal Person Reidentification (VT-ReID), where query and
gallery images come from different modalities. Existing works implicitly align
the modalities in pixel and feature spaces by either using adversarial learning
or carefully designing feature extraction modules that heavily rely on domain
knowledge. We propose a simple but effective framework, MMD-ReID, that reduces
the modality gap by an explicit discrepancy reduction constraint. MMD-ReID
takes inspiration from Maximum Mean Discrepancy (MMD), a widely used
statistical tool for hypothesis testing that determines the distance between
two distributions. MMD-ReID uses a novel margin-based formulation to match
class-conditional feature distributions of visible and thermal samples to
minimize intra-class distances while maintaining feature discriminability.
MMD-ReID is a simple framework in terms of architecture and loss formulation.
We conduct extensive experiments to demonstrate both qualitatively and
quantitatively the effectiveness of MMD-ReID in aligning the marginal and class
conditional distributions, thus learning both modality-independent and
identity-consistent features. The proposed framework significantly outperforms
the state-of-the-art methods on SYSU-MM01 and RegDB datasets. Code will be
released at https://github.com/vcl-iisc/MMD-ReID
