Abstract
Fine-grained image recognition (FGIR) is an important part of today's vision field. Unlike basic image classification, FGIR aims at recognizing multiple different dependent categories of meta-category objects. However, early FGIR faced many challenges, such as the fact that the differences between fine-grained images are subtle and the background is often noisy, which greatly affects the accuracy of the recognition performance. Now, with the cconstant development of deep learning in the field of optical recognition, FGIR has made large accomplishments and progress. In this paper, we make a more broad survey of deep learning-based FGIR methods in recent years and categorize these new methods. Then they are comparatively analyzed through recognized datasets in recent years to summarize the contribution of different methods to the current challenges. Eventually, the current state of research is analyzed and several directions for future research are put forward.
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