# Local Diversity-Guided Weakly Supervised Fine-Grained Image Classification Method
## 4 Results and Discussion
### Ablation Analysis  
In order to better understand the impact of each of the modules proposed in the LDGNet, we added the MASF module and the RSS strategy, respectively, to the categorization backbone in our experiments on the ancient towers dataset. We also added the CAIM  
Figure 6: The real-world environment of the ancient towers dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Backbone** & **Input Size** & **Accuracy** \\ \hline Resnet50 & Resnet50 & 448 \(\times\) 448 & 92.3\% \\ Resnet101 & Resnet101 & 448 \(\times\) 448 & 93.4\% \\ Densenet161 & Densenet161 & 448 \(\times\) 448 & 93.6\% \\ ViT [26] & ViT-B\_16 & 448 \(\times\) 448 & 93.8\% \\ PMG [16] & Resnet50 & 448 \(\times\) 448 & 94.2\% \\ FBSD [47] & Densenet161 & 448 \(\times\) 448 & 94.5\% \\ TransFG [46] & ViT-B\_16 & 448 \(\times\) 448 & 94.5\% \\ FFVT [28] & ViT-B\_16 & 448 \(\times\) 448 & 94.8\% \\ Ours & ConvNeXt & 448 \(\times\) 448 & **95.2\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the accuracy of different algorithms on ancient towers. Bold values represent the best performing data for each column. module and the RSS strategy to the categorization backbone, respectively. First, ConvNeXt was used as the test backbone. As shown in Table 6, the accuracy is slightly improved by adding the MASF module or RSS strategy. The structure with only the MASF added is shown in Figure 7b and the structure with only RSS added is shown in Figure 7c. The last row in Table 6 shows that the LDGNet improves the accuracy of the backbone by 2.1%, proving the effectiveness of the module. In order to investigate the functionality of the different model structures, we further investigated the variation of the visualization regions. First, we tested the performance of ConvNeXt; the structure is shown in Figure 7a and the corresponding heatmap is shown in Figure 8b. The heat map of the original backbone can basically cover the whole of the target to be detected, but cannot localize the discriminative region. Then, we added the MASF module to the backbone; the structure is shown in Figure 7b, and the corresponding heatmap is shown in Figure 8c. It can be seen that the cross-layer attentional interaction of the MASF can focus the attention to a small region and effectively utilize multi-scale features. Next, we added the RSS strategy to the backbone, as shown in Figure 7c. We can see from the heat map shown in Figure 8d that RSS can suppress the background features and tap into the potential detailed features in the process of random discard and enhancement, but it may also lead to a narrow sensory field. Finally, the structure of the LDGNet is shown in Figure 7d and the heat map in Figure 8e. The MASF establishes cross-layer feature relationships in space and channels, and RSS further improves feature richness and avoids convergence, therefore the overall network classification performance is optimal. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **CUB-200-2011 [24]** & **NABirds [35]** & **Ancient Towers** \\ \hline ConvNeXt & 91.2\% & 70.9\% & 93.1\% \\ ConvNeXt + MASF & 91.7\% (+0.5\%) & 90.9\% (+0.8\%) & 94.3\% (+1.2\%) \\ ConvNeXt + RSS & 91.4\% (+0.2\%) & 90.6\% (+0.5\%) & 93.8\% (+0.7\%) \\ ConvNeXt + MASF + RSS & 92.3\% (+1.1\%) & 91.5\% (+1.4\%) & 95.2\% (+2.1\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental results of ablation on three datasets. Figure 7: The structure of models, (**a**) original backbone, (**b**) backbone + MASF, (**c**) backbone + RSS, (**d**) backbone + LDGNet. Randomly Selected Strategy Hyper Parameter Settings  
This section will determine the hyper parameter threshold \(\delta\), and the probability ratio \(\alpha:\beta:\gamma\) of the three candidate branches. The threshold rate \(\delta\) is used to generate an elimination mask, where pixels greater than the threshold are set to 0, and pixels smaller than the threshold are set to 1. Since we needed to retain usable valid information, we initially set its value to 0.5.