TrustReID: Learning Which Identity Evidence to Trust for Universal Person Re-Identification

Person re-identification (ReID) requires matching identities across changes in viewpoint, clothing, sensing modality, and visibility. In unlabeled videos, strict association criteria fragment identities across views, whereas permissive criteria introduce incorrect identity merges, compromising supervision for pre-training. During matching, visual cues that distinguish identities in one setting may mislead retrieval in another, making fixed evidence fusion inadequate for a shared model. We introduce TrustReID, a two-stage framework that addresses these challenges through view-conditioned identity pre-training and counterfactual evidence routing. In Stage I, View-Conditioned Progressive Identity Discovery (VPID) preserves view-specific prototypes and progressively links observations within and across videos using relation-specific criteria. This process assigns high-purity pseudo-identity labels to over six million person images, supporting transferable identity learning with identity-supervised losses. Under matched data and computational budgets, this identity-supervised objective outperforms contrastive pre-training. In Stage II, Counterfactual Evidence Routing (CER) complements an always-on pretrained identity branch with four experts encoding color, texture, body structure, and visible parts. A shared, task-agnostic router weights these experts for each query–gallery pair. In addition to the final retrieval loss, we supervise expert weights using the change in the positive-negative ranking margin when each expert is masked, guiding the model to select evidence that improves identity ranking. Without downstream fine-tuning, the frozen pretrained model achieves Rank-1/mAP of 77.28%/51.18% on MSMT17, 70.58%/63.84% on AG-ReID v1 under ground-to-aerial retrieval, and 97.70%/96.25% on Occluded-ReID. After joint training, TrustReID outperforms several existing unified ReID models on multiple benchmarks, while supporting six ReID settings with a single checkpoint. We will publicly release the pre-training dataset and model weights to facilitate reproducibility and future research.