Researches advanced in fine-grained image classification based on deep learning

Abstract

In the recent past, the task of fine-grained image recognition has become a hot research topic for scholars. Due to its substantial intra-class and little inter-class variance, fine-grained image classification task is more difficult than traditional image classification task. From a deep learning standpoint, this paper first introduces representative algorithms for strongly supervised fine-grained image classification, consisting of the Part-based R-CNN algorithm, the Pose Normalized CNN algorithm, and the Mask-CNN model. Second, weakly supervised process in image classification is expounded from the representative methods of attention mechanism, end-to-end visual coding, zero-shot learning with adaptive domain decomposition. Third, in the experimental part, the most popular databases for precise picture categorization, the evaluation metrics and conclusion of the algorithms are listed. Finally, it summarizes and looks forward, points out the problems and deficiencies of current research, and gives guidance for possible solutions in the future.