# ConvNeXt-Based Fine-Grained Image Classification and Bilinear Attention Mechanism Model
## 2 Related Work  
### Analysis of Traditional Methods  
At present, there are two algorithms that can achieve fine-grained image classification. The first one is the classification algorithm based on strongly supervised learning. As this method is costly, time consuming and labor intensive, this method is gradually being replaced by a classification algorithm based on weak supervision. The main advantage of this algorithm is that only the class label of the image is used; no additional annotation is required for training. The network's ability to extract features and locate features and then achieve fine-grained image classification is improved by using special network mechanisms, such as residual, bilinear, and attention methods, together with special processing methods, such as bilinear pooling, data enhancement (such as Mixup and Cutmix), or bilinear cutting technology through the inductive bias characteristics of CNN convolution.  
The use of weakly supervised learning for fine-grained image classification is a general current trend. The most representative technique is the integration of the cascade attention mechanism based on a CNN network proposed by [PERSON] et al. [17] The network has three modules: spatial confusion attention, cross network attention and network fusion attention. They complement each other and are jointly trained and optimized. [PERSON] et al. [18] proposed an attribute-guided attention network. They use long short-term memory (LSTM) to gradually accumulate the learned information for judging semantics from the starting node to the deeper ones, providing a feature representation method that can be used for both judgement and learning. The classification accuracy of this algorithm on the fine-grained bird dataset CUB200-2011 reaches 85.1%. The bilinear convolutional neural network (BCNN) is a network model designed by [PERSON] et al. [19] for fine-grained image classification tasks. Figure 1 presents the structure chart of a bilinear network. It shows two feature extractors, i.e., CNN1 and CNN2. After the outputs of CNN1 and CNN2 are processed and pooled by the outer product, the softmax activation function outputs the prediction results. The classification accuracy of this model on the fine-grained bird dataset CUB200-2011 reaches 84.1%.  
[PERSON] et al. [20] put forward the hierarchical bilinear pooling (HBP) method, which regards each step of convolution in a CNN as an extraction operation for component features. The convolution output features of different layers are aggregated through bilinear pooling to build an interaction model between feature layers to obtain more subtle target fine-grained features. [PERSON] et al. [21] proposed a bilinear network based on an attention mechanism (BAM BCNN) according to the bilinear nature of the BCNN. It uses two-level attention to obtain object regions and component regions, extracts images in different stages, and adds an attention mechanism through a bilinear network.  
Referring to relevant research on fine granularity and network-optimization methods, in this paper, ConvNeXt is applied to the fine-grained classification task and further optimized and rectified so that it can accurately locate the fine-grained features and perform highly accurate fine-grained classification.  
A sequential block diagram of the research ideas in this paper is shown in Figure 2.