Fine-Grained Image Retrieval is an important problem in computer vision. It is more challenging than the task of content-based image retrieval because it has small diversity within the different classes but large diversity in the same class. Recently, the cross entropy loss can be utilized to make Convolutional Neural Network (CNN) generate distinguish feature for Fine-Grained Image Retrieval, and it can obtain further improvement with some extra operations, such as Normalize-Scale layer. In this paper, we propose a variant of the cross entropy loss, named Piecewise Cross Entropy loss function, for enhancing model generalization and promoting the retrieval performance. Besides, the Piecewise Cross Entropy loss is easy to implement. We evaluate the performance of the proposed scheme on two standard fine-grained retrieval benchmarks, and obtain significant improvements over the state-of-the-art, with 11.8% and 3.3% over the previous work on CARS196 and CUB-200-2011, respectively.

细粒度图像检索是计算机视觉中的重要问题。与基于内容的图像检索相比，它更具挑战性，因为它在不同类别中具有较小的多样性，而在同一类别中具有较大的多样性。近年来，交叉熵损失可用于使卷积神经网络（CNN）生成细粒度图像检索的区别特征，并且可以通过一些额外的操作（例如归一化尺度层）获得进一步的改进。在本文中，我们提出了一种交叉熵损失的变体，称为分段交叉熵损失函数，以增强模型泛化能力并提高检索性能。此外，分段交叉熵损失易于实现。我们在两个标准的细粒度检索基准上评估了该方案的性能，