MoMI-RC: Identity-Aware Reliability Calibration and Multi-Order Modality Interaction for Multi-Modal Object Re-Identification

Multi-modal object Re-Identification (ReID) exploits complementary RGB, near-infrared, and thermal-infrared information to retrieve the same person or vehicle under challenging imaging conditions. However, fusion weights predicted solely from individual inputs may lack a stable identity-discriminative reference, while merging interactions from different modality combinations can obscure their distinct sources. To address these issues, we propose MoMI-RC, a multi-order modality interaction network with identity-aware reliability calibration. The reliability calibration module learns global modality anchors and bounded sample-wise corrections under supervision based on intra-class compactness and inter-class separation. The anchors provide a stable reference, while the corrections adapt modality contributions to individual inputs. The multi-order modality interaction module constructs compact pairwise and three-way interactions in a shared low-rank space, preserving each interaction in a separate coordinate block alongside calibrated unimodal features. Modality-subset simulation and consistency regularization further stabilize the descriptor during training. Extensive experiments demonstrate the effectiveness of MoMI-RC, which achieves 84.2%, 89.6%, and 70.9% mAP on RGBNT201, RGBNT100, and MSVR310, respectively, without task-specific text or segmentation priors.