TRIDA: Tri-modal Residual-aware Inter-modal Divergence Arbitration for Multi-spectral Object Re-identification

Multi-spectral object re-identification (ReID) aims to match identities across heterogeneous spectral observations. This matching capability is critical under varying illumination and adverse sensing conditions. Cross-spectral interaction is essential for exploiting complementary spectral cues, yet it is not cost-free: token selection discards potentially discriminative cues at unselected positions, all-token retention propagates modality-specific interference, and unconstrained interaction further incurs representation drift and gradient conflicts. We propose Tri-modal Residual-aware Inter-modal Divergence Arbitration (TRIDA), which addresses these challenges through three coordinated designs. Specifically, GRACE retains all tokens to preserve identity cues and reorganizes cross-modal information via channel-group cyclic permutation, while cross-modal CLS differences adaptively modulate CLS attention and residual scaling to mitigate modality-specific interference. GRASP then counters representation drift by regularizing group-wise deviations toward a sample-wise cross-modal center with progressive scheduling, while preserving discriminative modality-specific information. Finally, MCGA resolves conflicting gradients: it probes modality-wise gradients from the identity-discriminative and image-text objectives on the trainable GRACE enhancement parameters, and applies conflict-severity-adaptive, order-independent arbitration, leaving all unprobed gradient contributions intact. Through empirical evaluations on five multi-spectral object ReID benchmarks, TRIDA demonstrates superior object retrieval performance while requiring only modest parameter overhead.