###### Abstract  
The fine-grained image classification task is about differentiating between different object classes. The difficulties of the task are large intra-class variance and small inter-class variance. For this reason, improving models' accuracies on the task heavily relies on discriminative parts' annotations and regional parts' annotations. Such delicate annotations' dependency causes the restriction on models' practicability. To tackle this issue, a saliency module based on a weakly supervised fine-grained image classification model is proposed by this article. Through our salient region localization module, the proposed model can localize essential regional parts with the use of saliency maps, while only image class annotations are provided. Besides, the bilinear attention module can improve the performance on feature extraction by using higher- and lower-level layers of the network to fuse regional features with global features. With the application of the bilinear attention architecture, we propose the different layer feature fusion module to improve the expression ability of model features. We tested and verified our model on public datasets released specifically for  
like the types of vehicles (SUV, sedan, truck, and so on), or brand (Volkswagen, Mercedes Benz, BMW, or so) are wanted but also more accurate and fine-grained information like the models of vehicles (Volkswagen 2006-2011, Volkswagen Sagitar 2006-2011, Volkswagen Sagitar 2012-2014, BMW 3 Series 2013-2014, and so on) are wanted. This finer-grained information will provide great help for case investigation, tracking accident escape, deck, and fake vehicles to traffic law enforcement departments. Hence, with this help, governments can maintain or even improve social stability. For this reason, fine-grained image classification has great research value and broad application prospects. Fine-grained image classification is an important research topic in the field of computer vision, which aims to perform lower-level fine-grained classification upon higher-level coarse-grained categories. The main challenge of fine-grained classification is that the differences between different subcategories are usually subtle and local. In the related studies, it is usual to pre-process images for the extraction of image features, like color, texture, and contour. Then, the extracted features are used for training different models. During the test process, the same pre-processing procedure is applied to the testing images for the extraction of features, and these features are fed into the trained model to achieve classification results. Therefore, in the early stage, the bag-of-words method was first proposed [3]. Traditional artificial features are fed into the model to extract the corresponding feature vectors and obtain the final classification results. [PERSON] et al. introduced the CUB200-2011 dataset [4] and proposed some benchmark methods. However, their classification method for uncropped images only achieved an accuracy of 10.3%. Their model was used to first locate regional areas, then apply the bag-of-words method for encoding two different kinds of feature, RGB histogram features, and scale invariant feature transform (SIFT) features [5]. The encoded features would be fed into a support vector machine (SVM) classifier for image classification training. Such low accuracy is not satisfying. This method is limited due to the regional area localization method they used being not accurate enough; also, the artificially designed features cannot distinguish enough. Hence, many researchers have proposed new feature descriptors based on their research, like part-based one-vs-one features (POOF) [6], Fisher-encoded SIFT [7], supervised kernel descriptors for visual recognition (KEDS) [8], etc. These methods, on fine-grained image classification, can reach an accuracy from 50% up to 62% [9; 10].