Generalization Limits in Vehicle Re-Identification
2026-06-01 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors studied how well current vehicle re-identification methods work when trying to recognize cars they haven't seen before, rather than just ones that look very similar to the training examples. They noticed that many tests let methods score well by memorizing similar vehicles, which doesn't show true generalization. To fix this, they created a new way to test that focuses on recognizing completely new vehicle types and also checks how well methods handle different viewing angles. Their results show most methods struggle with new vehicles and have limited ability to handle changes in viewpoint unless the vehicle types were seen during training.
vehicle re-identificationgeneralizationdataset splittingviewpoint robustnesstraining settest setmake and modelmemorizationevaluation protocol
Authors
Anis Yassine Ben Mabrouk, Antoine Tadros, Rafael Grompone von Gioi, Gabriele Facciolo, Axel Davy, Rodrigo Verschae
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.