# Local Diversity-Guided Weakly Supervised Fine-Grained Image Classification Method
## 1 Introduction  
Fine-grained image classification, also known as sub-category image classification, aims to achieve a more detailed sub-category delineation of images belonging to the same basic category (such as cars, dogs, flowers, birds, etc.). It has wide-ranging business requirements and practical applications in both industry and everyday life. Fine-grained image classification is more challenging than regular tasks due to subtle differences between classes and large variations within them. Its inherent difficulties primarily manifest in two key aspects: image recognition algorithms and the construction of application-specific databases [1]. 1. In the realm of image recognition algorithms, there are two primary directions: strong supervision and weak supervision. Strong supervised learning requires class labels, bounding boxes, and part location points during the model training process. Conversely, weakly supervised learning involves training models solely with category labels. For fine-grained tasks, the key challenge lies in enabling neural networks to pinpoint discriminative parts and learn distinguishable features. In the early stagesof research, strong supervision methods were commonly employed to address fine-grained problems. For instance, as described in [PERSON] et al. [2], a Part-based R-CNN (Region-based Convolutional Neural Network) classification algorithm was introduced. It leveraged bounding boxes and part annotation information in fine-grained images to train and obtain image block features at both object and part levels. These features are subsequently concatenated and used for classification. [PERSON] et al. [3] proposed a novel Part-Stacked CNN architecture. This approach employed a Fully Convolutional Network (FCN) to localize the central positions of each local region and utilized object-level labels to learn semantic information at the object level. Strong supervision methods rely heavily on manual annotations such as bounding boxes and part locations, making them impractical due to high annotation costs. With the development of deep learning and related research, the weakly supervised method, which only relies on category labels to complete classification without additional manual annotation information, can also achieve good classification performance. For example, the study by [PERSON] et al. [4] represents the first attempt to perform fine-grained image classification without utilizing additional manual annotations, relying solely on category labels. This algorithm primarily focuses on features at two levels: object-level and part-level. It combines the predictions from the object-level model and the results from the part-level model to generate the final output. In the study by [PERSON] et al. [5], a Gaussian mixture model is proposed to solve the local area diffusion problem, which also works better to locate the discernible regions. In the study by [PERSON] et al. [6], a Bilinear Convolution Network (BCNN) model is designed which combines two network features by multiplying the vector outer product to achieve a more discriminative fine-grained feature representation. On the one hand, this weakly supervised classification algorithm reduces the cost of additional manual labeling. On the other hand, it better meets the practical application requirements and the development trend of the current research. [PERSON] et al. [4] verified that an attention mechanism is beneficial to enhance the focus on discriminative parts and thus thereby improve the accuracy of classification tasks. [PERSON] et al. [7] proposed an Object-Part Attention Model (OPAM) to enhance the representation of image features through object-level and part-level attention mechanisms. [PERSON] et al. [8] utilized a multi-scale attention fusion approach to extract attention information at different levels of depth in the network and then merged this information at the output layer to generate attention maps. [PERSON] et al.