Vehicle Re-Identification (Re-ID) is challenging because vehicles of the same model commonly show similar appearance. We tackle this challenge by proposing a Global-Regional Feature (GRF) that depicts extra local details to enhance discrimination power in addition to the global context. It is motivated by the observation that, vehicles of same color, maker, and model can be distinguished by their regional difference, e.g., the decorations on the windshields. To accelerate the GRF learning and promote its discrimination power, we propose a Group-Group Loss (GGL) to optimize the distance within and across vehicle image groups. Different from the siamese or triplet loss, GGL is directly computed on image groups rather than individual sample pairs or triplets. By avoiding traversing numerous sample combinations, GGL makes the model training easier and more efficient. Those two contributions highlight this work from previous methods on vehicle Re-ID task, which commonly learn global features with triplet loss or its variants. We evaluate our methods on two large-scale vehicle Re-ID datasets, i.e., VeRi and VehicleID. Experimental results show our methods achieve promising performance in comparison with recent works.

中文翻译：

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基于组间损失的全球区域特征学习用于车辆重新识别。


车辆重新识别（Re-ID）具有挑战性，因为同一型号的车辆通常呈现相似的外观。我们通过提出全球区域特征（GRF）来应对这一挑战，该特征描述了除了全球背景之外的额外局部细节以增强区分能力。其动机是观察到，相同颜色、制造商和型号的车辆可以通过其区域差异（例如挡风玻璃上的装饰）来区分。为了加速 GRF 学习并提高其区分能力，我们提出了组-组损失（GGL）来优化车辆图像组内和之间的距离。与 siamese 或 Triplet Loss 不同，GGL 直接在图像组上计算，而不是在单个样本对或三元组上计算。通过避免遍历大量样本组合，GGL 使模型训练变得更容易、更高效。这两项贡献强调了之前车辆重新识别任务方法的工作，这些方法通常通过三元组损失或其变体来学习全局特征。我们在两个大型车辆重识别数据集（VeRi 和 VehicleID）上评估我们的方法。实验结果表明，与最近的工作相比，我们的方法取得了有希望的性能。