A Survey of Fine-Grained Image Classification Based on Deep Learning

Chun-feng GUO, Hai-rong CUI, Kun YU, Xin-ping MO


The deep learning technology has shown impressive performance in various vision tasks such as image classification and object detection. Recent advances of deep learning techniques bring encouraging performance to fine-grained image classification which aims to distinguish subordinate-level categories. This paper reviews the major deep learning concepts pertinent to fine-grained image analysis and summarizes to the field, briefly introduces the representative method of fine-grained image classification based on deep learning. At present, the field of fine classification has developed a variety of methods including multi-network learning, target part detection and visual attention. Each method is to obtain a distinguishing area in the image, which help the network learn more effective features to complete the classification and recognition of fine-grained images.


Deep learning, Fine-grained classification, Convolutional neural networks.


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