###### Abstract
From the early model design, we can see that the use of discriminative extracted image features and feature encoding methods have a significant impact on the final classification results, which means the better the methods that are used, the more discriminative features are used, and the better the classification results. So, it can be seen that locating the regional feature area of images plays an important part in achieving good classification results. However, annotating the regional feature area manually is expensive, resulting in obvious defects on practical applications. In recent years, deep neural networks have been widely used in the field of computer vision. According to the annotations used during training, deep neural network methods are divided into strongly supervised learning [11; 12; 13; 14; 15] and weakly supervised learning [16; 17; 18]. The strongly supervised fine-grained image classification method is characterized by not only image category tag data but also additional manual annotations, such as key points of annotations or regional area annotations that are provided during training. Thus, strongly supervised fine-grained image classification methods require additional manually annotated data, which makes these methods expensive and heavily restricts their application area. For these reasons, strongly supervised methods may not be the most appropriate choice for the actual classification tasks. Adopting the weakly supervised method is another big trend in the field of fine-grained image classification research. How to find the most discriminative regions has been studied by various researchers. A weakly supervised fine-grained classification network based on two-level attention is proposed by this article for tackling this problem. Our method consists of two parts. One of them is based on salient region localization module. This module is designed for locating discriminative regions, and it is trained while only using images' categories as labels. The other one is the bilinear attention module for fusing regional and global features. This module is used for extracting regional and global feature from higher- and lower-level layers of bilinear neural networks separately. Then, we fuse these features to improve the features' representation capability and construct our different layer feature fusion module. The main contributions of our study are as follows:  
1. The differences between different subcategories are usually subtle and local. Hence, how to locate and distinguish the area has become key to solving the problem. An essential new regional area localization method (salient region localization module) is proposed, which can accurately locate and extract the most distinct regional areas and reduce the dependence of manual annotation information. 2. We adopt the bilinear neural network for the extraction of global features and regional features, which allows us to make better use of global features and regional features for training. The use of the bilinear neural network allows us end-to-end train our model. Besides, using bilinear neural network makes our model more stable. 3. Due to huge intra-class variance and small inter-class variance, a different layer feature fusion module is proposed. First, we add center loss to our loss function to improve the distinction between classes. In this way, we can reduce the impact of large intra-class variance and small inter-class variance. Finally, we better guide the fine-grained image classification by combining low-level visual features and advanced semantic information. 4. Our resulted model is trained without providing manually annotated essential areas while reaching an accuracy of 85.1% on the CUB-200-2001 dataset. Our method's resulting accuracy is better than most strongly supervised method ones. This result shows that our model can reduce the dependence on delicate manually annotated essential areas while maintaining acceptable accuracy. The rest of this article is organized as follows.