Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications. The task of FGIA targets analyzing visual objects from subordinate categories, e.g., species of birds or models of cars. The small inter-class and large intra-class variation inherent to fine-grained image analysis makes it a challenging problem. Capitalizing on advances in deep learning, in recent years we have witnessed remarkable progress in deep learning powered FGIA. In this paper we present a systematic survey of these advances, where we attempt to re-define and broaden the field of FGIA by consolidating two fundamental fine-grained research areas - fine-grained image recognition and fine-grained image retrieval. In addition, we also review other key issues of FGIA, such as publicly available benchmark datasets and related domain-specific applications. We conclude by highlighting several research directions and open problems which need further exploration from the community.

中文翻译：

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深度学习的细粒度图像分析：一项调查。

细粒度图像分析 (FGIA) 是计算机视觉和模式识别中长期存在的基本问题，是各种现实应用的基础。FGIA 的任务目标是分析从属类别的视觉对象，例如，鸟类种类或汽车模型。细粒度图像分析固有的小类间和大类内变化使其成为一个具有挑战性的问题。近年来，利用深度学习的进步，我们见证了深度学习驱动的 FGIA 取得的显着进步。在本文中，我们对这些进展进行了系统的调查，我们试图通过巩固两个基本的细粒度研究领域——细粒度图像识别和细粒度图像检索——来重新定义和拓宽 FGIA 的领域。此外，我们还审查了 FGIA 的其他关键问题，例如公开可用的基准数据集和相关的特定领域应用程序。最后，我们强调了几个需要社区进一步探索的研究方向和未解决的问题。