Unity in Diversity: Multi-expert Knowledge Adversarial Learning and Collaboration for Generalizable Vehicle Re-identification.
Generalizable vehicle re-identification (ReID) seeks to develop models capable of adapting to previously unseen domains without additional fine-tuning or retraining. Most existing approaches attempt to learn domain-invariant representations by aligning data distributions across source domains. However, they often neglect the inherent domain-related redundancy within source images, which suppresses the learning of complementary features characterized by lower occurrence probabilities and weaker activations. To overcome this limitation, we introduce Unity in Diversity (UID), a framework of multi-expert knowledge adversarial learning and collaboration. UID incorporates a training-free mechanism to filter out domain-related redundancy in source images, thereby promoting the learning of complementary feature representations. Specifically, we design a Spectrum-based Transformation for Redundancy Elimination and Augmentation Module (STREAM), which generates two distinct types of image inputs for a two-stage complementary feature learning process. In the multi-expert knowledge adversarial learning phase, STREAM enables the model to acquire a diversified identity-oriented prompt set that captures subtle but discriminative visual cues critical for distinguishing highly similar vehicles. This multi-expert prompt set is progressively integrated into complementary feature representations through the proposed knowledge confrontation and collaboration mechanism, which substantially enhances the model's ability to extract fine-grained and complementary information. Extensive experiments conducted on multiple benchmarks demonstrate that UID achieves state-of-the-art performance, validating its effectiveness and generalizability. Our code is available at https://github.com/KZYYYY/UID.