提出新评估方法，更好衡量车辆重识别的泛化能力。
Generalization Limits in Vehicle Re-Identification
- 设计新评测方案，区分视角鲁棒性与车型泛化能力
- 发现主流方法对未见车型泛化能力差
- 适合关注模型真实泛化性能的研究者
车辆重识别旨在给定查询图像后从图库中检索同一辆车的图像。通过对常用数据集的深入分析，我们发现训练集和测试集中均存在视觉差异极小的车辆（如相同品牌、型号、颜色）。这导致依赖记忆训练数据的方法在这些测试集上表现良好，却难以推广到其他数据集。本文提出一种新型评估方法，更有效地衡量模型对未见车型的泛化能力；同时，基于视角划分评估，以区分视角鲁棒性与同视角重识别的影响。结果表明，多数前沿方法在未见车型上表现不佳，且其视角鲁棒性和细节关注能力仅限于训练中见过的车型。
原文摘要 · Abstract (English)
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.
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