# Seg-Eigen-CAM: Eigen-Value-Based Visual Explanations for Semantic Segmentation Models
## 4 Experiments
### Quantitative Analysis  
In this section, we evaluate the effectiveness of various interpretability methods using a comprehensive evaluation framework to quantitatively assess their explanation performance. This framework enables a multidimensional evaluation of explanation capabilities, providing an in-depth analysis of the advantages and limitations of different methods. Table 5 presents the results of the comprehensive evaluation of different explanation methods in three semantic segmentation models applied to all images in the dataset. The experimental results indicate that Seg-XRes-CAM exhibits the best performance in \(Dice_{H}\), achieving the highest scores in most categories in all three models. This demonstrates its ability to accurately capture the regions of interest influencing the model's decision, effectively utilizing spatial information to generate local explanations. This results in high consistency and fidelity, with the attention regions aligning closely with the predicted regions. However, even less precise XAI methods often produce large highlighted areas in \begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Method** & **Model** & **Eigen-CAM** & **Seg-Grad-CAM** & **Seg-XRes-CAM** & **Ours** \\ \hline \multirow{3}{*}{\(MeanDice_{H}\)} & DeepLabV3 & 0.49 & 0.75 & **0.80** & 0.69 \\  & PSPNet & 0.47 & 0.79 & **0.81** & 0.69 \\  & BiSeNetV1 & 0.44 & 0.54 & **0.60** & 0.55 \\ \hline \multirow{3}{*}{\(MeanEXAI\)} & DeepLabV3 & 0.12 & 0.14 & **0.07** & 0.13 \\  & PSPNet & **0.17** & 0.28 & 0.23 & 0.31 \\  & BiSeNetV1 & 0.11 & 0.15 & 0.17 & **0.10** \\ \hline \multirow{3}{*}{Mean PE} & DeepLabV3 & 1.19 & 1.88 & 1.19 & **5.34** \\  & PSPNet & 1.27 & 1.23 & 1.22 & **5.01** \\ \cline{1-1}  & BiSeNetV1 & 1.46 & 2.00 & 1.21 & **4.38** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comprehensive evaluation results of XAI methods. The bold font number indicate the highest value corresponding to each label. Figure 6: Visualization of the impact of evaluation metrics under different methods. the image, which may overlap with the predicted regions, thus preventing extremely low \(Dice_{H}\) scores. This phenomenon is observable in Eigen-CAM: although its explanations may not align with intuitive human judgments and may even be deemed incorrect, it still maintains a certain level of \(Dice_{H}\). In addition, all methods perform poorly on BiSeNetV1 in terms of \(Dice_{H}\), probably due to the lower baseline performance of BiSeNetV1 as a lightweight semantic segmentation model, as shown in Table 1. This highlights the impact of the performance of the underlying model on the explanation results. For the \(E_{XAI}\) metric, Seg-XRes-CAM and our proposed method demonstrate superior performance in individual class evaluations, with lower values of \(E_{XAI}\). This suggests that these methods are more effective in identifying key features that influence the model's segmentation decisions, excelling in local interpretability. However, our method also faces challenges: By retaining only the primary directional information in the image regions, some details may be overlooked. When the masked image is re-predicted, this may lead to prediction failure, with some classes exhibiting a higher increase in entropy. This phenomenon reflects the instability that can arise under low image retention rates, which also explains why our overall average performance is lower than Seg-XRes-CAM. For the \(PE\) metric, our proposed method outperforms all other methods in all models and classes. As shown in Table 6, our method achieves significantly lower image retention rates while maintaining stable performance \(Dice_{H}\), demonstrating its ability to effectively identify the core decision-making regions. In contrast, Seg-XRes-CAM shows a higher \(Dice_{H}\), indicating that it can faithfully present the rationale of the model decision. However, because the explanation is coarser and the influence of gradient weighting and gradient noise, Seg-XRes-CAM exhibits comparatively poorer performance in the \(PE\) metric.