Data Augmentation Vision Transformer for Fine-grained Image Classification
This work improves fine-grained image classification for computer vision applications, but it is incremental as it builds on existing vision transformer methods.
The paper tackled the problem of fine-grained image classification by addressing limitations in vision transformers, such as ignoring local features and introducing noise, resulting in accuracy improvements of 1.4% and 1.6% over the original ViT on CUB-200-2011 and Stanford Dogs datasets.
Recently, the vision transformer (ViT) has made breakthroughs in image recognition. Its self-attention mechanism (MSA) can extract discriminative labeling information of different pixel blocks to improve image classification accuracy. However, the classification marks in their deep layers tend to ignore local features between layers. In addition, the embedding layer will be fixed-size pixel blocks. Input network Inevitably introduces additional image noise. To this end, we study a data augmentation vision transformer (DAVT) based on data augmentation and proposes a data augmentation method for attention cropping, which uses attention weights as the guide to crop images and improve the ability of the network to learn critical features. Secondly, we also propose a hierarchical attention selection (HAS) method, which improves the ability of discriminative markers between levels of learning by filtering and fusing labels between levels. Experimental results show that the accuracy of this method on the two general datasets, CUB-200-2011, and Stanford Dogs, is better than the existing mainstream methods, and its accuracy is 1.4\% and 1.6\% higher than the original ViT, respectively