With the growth of image classification models and the development of deep learning, the level of computer image classification has basically surpassed that of humans, but the level of fine-grained computer image classification is still weaker than that of humans, so fine-grained image classification is currently given a lot of attention. This paper carries out visualized analysis for the fine-grained image classification in terms of cooperation, scientific research, research trend and other aspects by CiteSpace software, and also points out that fine-grained image classification relies on CNN while looking into Transformer. However, fine-grained image classification has the problems of model reliance on prior knowledge (CNN), lack of sufficient data (Transformer), and lack of local relevance (CNN/Transformer), which can be solved by reducing the complexity of the model, transfer learning data augmentation, combination of CNN with Transformer, etc. On this basis, this paper indicates that Swin-Transformer has good generalization, hierarchy and translation invariance, and will become the main research direction for fine-grained image classification in the future.
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