Generalization Limits in Vehicle Re-Identification

2026-06-01

AI Summary

This work addresses the limited generalization of existing vehicle re-identification methods to unseen vehicle models, despite their strong performance when training and test vehicles are similar. To systematically evaluate model robustness in open-world scenarios, the authors propose a novel evaluation protocol tailored for unseen vehicle models. This protocol employs a viewpoint decoupling strategy to disentangle viewpoint robustness from same-view identification performance and integrates data distribution analysis with cross-dataset evaluation. Experimental results reveal a significant drop in generalization performance of state-of-the-art methods on unseen models, demonstrating that their viewpoint invariance and fine-grained detail perception are heavily dependent on vehicle models present during training. These findings underscore the substantial limitations of current approaches in realistic, open-set deployment settings.

Abstract

Vehicle re-identification focuses on retrieving images of the same vehicle from a gallery given a query image. Upon closer inspection of commonly used datasets, we observe that vehicles with few visual differences-e.g., the same make, model, and color-appear in both the training and test sets. As a result, methods that effectively memorize the training data tend to perform well on these test sets but struggle to generalize to other datasets. In this paper, we address this issue by proposing a novel evaluation approach that more effectively measures generalization capability to unseen vehicle types. To further study generalization performance, we also propose splitting the evaluation based on view, allowing us to differentiate the effect of viewpoint robustness from that of same-view re-identification. Our findings reveal that most state-of-the-art methods struggle with unseen vehicle types, and that their robustness to viewpoint changes and attention to detail are limited to vehicle types seen during training.

Problem

• vehicle re-identification
• generalization
• unseen vehicle types
• viewpoint robustness
• dataset bias

Innovation

• vehicle re-identification
• generalization
• unseen vehicle types
• viewpoint robustness
• evaluation protocol