Timur Mamedov

The paper proposes a decoupled two-stage training pipeline to effectively learn a shared representation for person re-identification by mitigating optimization conflicts between image-based and text-based modalities.

The paper identifies a fundamental mismatch between standard pairwise ranking metrics (like AP and FPR-95) and the true assignment objective in multi-view object association, proposing a Sinkhorn-based normalization to correct this discrepancy.