Deformable Convolutional Neural Network for Fine-grained Image Recognition
Peiling Jiang · 2019 3rd International Conference on Electronic Information Technology and Computer Engineering (EITCE) · 2019
In recent years, deep learning techniques have demonstrated their powerful feature extraction capabilities in a variety of visual analysis tasks. Especially in the field of fine-grained image classification, deep feature extraction technology has achieved great performance improvement. The depth features extracted based on the deep learning model have strong expressive power. Fine-grained image classification is intended to subclass large categories, such as different types of birds. This identification task is more challenging because these sub-classes have large interclass differences and small inter-class differences. This paper first introduces the research progress of fine-grained image classification. Then, the data set used in the experiment and the extended data set are introduced. A variable convolution method for fine-grained image classification is proposed, and the research direction of the next step is summarized and explored.