  Visible-infrared person re-identification (VI-ReID) is a challenging task due
to large cross-modality discrepancies and intra-class variations. Existing
methods mainly focus on learning modality-shared representations by embedding
different modalities into the same feature space. As a result, the learned
feature emphasizes the common patterns across modalities while suppressing
modality-specific and identity-aware information that is valuable for Re-ID. To
address these issues, we propose a novel Modality Unifying Network (MUN) to
explore a robust auxiliary modality for VI-ReID. First, the auxiliary modality
is generated by combining the proposed cross-modality learner and
intra-modality learner, which can dynamically model the modality-specific and
modality-shared representations to alleviate both cross-modality and
intra-modality variations. Second, by aligning identity centres across the
three modalities, an identity alignment loss function is proposed to discover
the discriminative feature representations. Third, a modality alignment loss is
introduced to consistently reduce the distribution distance of visible and
infrared images by modality prototype modeling. Extensive experiments on
multiple public datasets demonstrate that the proposed method surpasses the
current state-of-the-art methods by a significant margin.
