# Local Diversity-Guided Weakly Supervised Fine-Grained Image Classification Method
## 2 Related Work  
### Fine-Grained Visual Classification  
Unlike typical image classification tasks, fine-grained images usually have low signal-to-noise ratios, where the discriminative information is concentrated in very small localized regions. Therefore, the focus of fine-grained tasks [1] is locating distinguishable parts and learning distinguishable features. Benefitting from advancements in deep learning technology, the feature extraction capabilities of neural networks have significantly improved. Recent research on fine-grained tasks has shifted from strong supervision methods,which rely on additional manual annotations such as bounding boxes and part location information, to weak supervision methods, where classification is achieved solely based on category labels. NTS-Net [15] used a region proposal network to candidate top-K informative regions from the feature map and integrate them with the original feature map. [PERSON] et al. [16] used Jigsaw Puzzle Generator and the Progressive Training (PT) strategy to generate images with different granularity levels. [PERSON] et al. [17] proposed a filtering and distillation learning model by constraining the object region to effectively improve the region focus. [PERSON] et al. [18] proposed an association learning model based on graph propagation, which simultaneously considers global image-level context and local spatial context. The model also explores the internal semantic correlation between different regional features. It is evident that effectively utilizing local information and constructing more robust feature representations are key breakthroughs in fine-grained tasks. [PERSON] et al. [19] proposed a graph-based higher-order relationship discovery method to uncover complex relationships both within and between images for fine-grained recognition. To address the inter-class similarity issue, [PERSON] et al. [20] introduced a pairwise confusion optimization process and proposed an end-to-end approach for fine-grained image classification. [PERSON] et al. [21] found that directly applying ViT for fine-grained image classification leads to excessive noise during the training phase and proposed a new ViT-based framework, ACC-ViT. One of its core ideas is to segment information-rich images into new graphs to avoid the impact of the background. Additionally, the performance of fine-grained visual classification can be improved by fusing features from different layers and introducing cross-layer regularization [22]. Fine-tuning models from large-scale datasets to small-scale ones using transfer learning and domain-specific data also achieves excellent classification results [23]. Although the above studies have made varying degrees of progress in fine-grained tasks, they still face limitations in mining local information and improving feature robustness. In this paper, we combine the advantages of spatial attention and channel attention to effectively capture the relative importance between different regions and channels in the feature map, which helps to uncover more local information. Considering the robustness of features, we perform channel-space attention interaction at different levels of feature maps and conduct cross-reorganization, allowing the information from different levels to merge and generate more discriminative features.