  A key challenge in visible-infrared person re-identification (V-I ReID) is
training a backbone model capable of effectively addressing the significant
discrepancies across modalities. State-of-the-art methods that generate a
single intermediate bridging domain are often less effective, as this generated
domain may not adequately capture sufficient common discriminant information.
This paper introduces the Bidirectional Multi-step Domain Generalization
(BMDG), a novel approach for unifying feature representations across diverse
modalities. BMDG creates multiple virtual intermediate domains by finding and
aligning body part features extracted from both I and V modalities. Indeed,
BMDG aims to reduce the modality gaps in two steps. First, it aligns modalities
in feature space by learning shared and modality-invariant body part prototypes
from V and I images. Then, it generalizes the feature representation by
applying bidirectional multi-step learning, which progressively refines feature
representations in each step and incorporates more prototypes from both
modalities. In particular, our method minimizes the cross-modal gap by
identifying and aligning shared prototypes that capture key discriminative
features across modalities, then uses multiple bridging steps based on this
information to enhance the feature representation. Experiments conducted on
challenging V-I ReID datasets indicate that our BMDG approach outperforms
state-of-the-art part-based models or methods that generate an intermediate
domain from V-I person ReID.
