# Local Diversity-Guided Weakly Supervised Fine-Grained Image Classification Method
## 1 Introduction
[9] proposed a trilinear attention sampling network, including an attention module, an attention sampler, and a feature distiller. [PERSON] et al. [10] introduced an attention-based Convolutional Binary Tree structure suitable for fine-grained recognition. In this tree structure network, attention transformers were incorporated to encourage the network to capture discriminative features. Node-to-node routing functions were used as decision paths to achieve hierarchical feature learning from coarse to fine. While attention mechanisms guide models to focus on discriminative parts, the above-mentioned methods often concentrate on a few salient regions and do not comprehensively explore potentially discriminative parts. Additionally, they treat individual features in isolation. Therefore, this paper posits that effectively mining local information and enhancing the abundance of the acquired information are effective ways to improve feature extracting abilities. Based on the above analysis, this paper proposes a Local Diversification Guidance Weak Supervised Classification Network (LDGNet). It incorporates a Multi-Attention Semantic Fusion Module (MASF) that utilizes convolution operations to generate attention maps at different levels, representing various parts of the target object. Additionally, it performs cross-layer attention interactions to enhance the semantic expression of these attention maps. Meanwhile, to avoid the generated attention maps from focusing on the same salient regions, random selection strategy (RSS) is proposed in the network training phase to force the attention maps to focus on multiple different distinguishable parts. Finally, to create a more comprehensive description of fine-grained details, bilinear pooling (BP) is used to integrate feature maps with attention maps into the classification recognition network. 2. In the domain of database acquisition, acquiring fine-grained image data is significantly more challenging than conventional classification tasks. Fine-grained image data are often indistinguishable by ordinary individuals and requires domain experts with relevant knowledge to perform information labeling, which incurs substantial labor costs. The main databases used for fine-grained image studies include datasets such as Stanford Dogs [11], FGVC Aircraft [12], Stanford Cars [13], and CUB-200-2011 [14]. While these datasets have many categories and high-quality annotations, they do not consist of original images captured in real-life scenarios. There are significant differences between the images captured by smartphones and the images in the dataset, which limits the application scenarios of the existing recognition results. Therefore, constructing fine-grained image datasets from natural scenes and closely aligning them with practical applications is a worthwhile endeavor in this field. Chinese ancient towers exhibit specific forms and styles, making instance recognition of natural scene ancient towers a quintessential fine-grained classification challenge. Based on this premise, this paper has curated a Chinese ancient towers dataset, encompassing famous/typical ancient tower structures from 34 provincial-level administrative regions in China. These data are sourced from natural scenes and include various real-life interference factors such as different angles, times, and scenes. This dataset is designed for conducting highly similar ancient building classification research, providing a new direction for the practical application of fine-grained tasks. To sum up, the main contributions of this paper are as follows: 1. The method of feature interaction is established to strengthen multi-level attentional association and enhance the ability of semantic complementary information mining. 2. In this paper, we propose a random selection strategy to design three operations, \"most significant suppression\", \"feature enhancement\".