# Seg-Eigen-CAM: Eigen-Value-Based Visual Explanations for Semantic Segmentation Models
## 4 Experiments
### Ablation Study
#### 4.4.2 Sign Correction Strategy Experiments  
In this section, we conduct an ablation study to analyze the effect of the sign correction strategy on the interpretability of the model. Table 4 presents the detailed results of this experiment. As shown in Figure 5, we select a scene image that contains both the target class (person) and various background regions for analysis. Without sign correction, the model exhibits a strong negative response in the target class region while displaying a weaker positive response in non-target background areas. This contradicts intuitive explanations. In contrast, after applying sign correction, the model's attention is more effectively concentrated on the person, producing a visual explanation that aligns with human perception.  
The experimental results indicate that, due to the inherent symbolic ambiguity in the feature space, uncorrected saliency maps may lead to instability and inconsistency in explanation results. Specifically, this can cause the model to overemphasize background regions while suppressing truly important areas. By incorporating the sign correction strategy, we successfully mitigate this issue, enabling the model to generate more stable and intuitive explanations.  
\begin{table}
\begin{tabular}{l c c|l c c} \hline \hline  & \multicolumn{4}{c}{**Mean Dice\({}_{H}\)**} \\ \hline
**Label** & \(\mathbf{Strategy_{1}}\) & \(\mathbf{Strategy_{2}}\) & **Label** & \(\mathbf{Strategy_{1}}\) & \(\mathbf{Strategy_{2}}\) \\ \hline Person & **0.82** & 0.81 & Bear & 0.58 & **0.58** \\ Car & **0.61** & 0.60 & Zebra & 0.84 & **0.84** \\ Motorcycle & 0.80 & **0.80** & Giraffe & 0.78 & **0.79** \\ Airplane & 0.65 & **0.66** & Umbrella & 0.69 & **0.69** \\ Bus & **0.69** & 0.69 & Bowl & **0.38** & 0.38 \\ Train & 0.69 & **0.70** & Pizza & 0.68 & **0.68** \\ Truck & 0.54 & **0.55** & Chair & **0.35** & 0.35 \\ Boat & 0.54 & **0.55** & Couch & 0.46 & **0.47** \\ Traffic Light & 0.53 & **0.54** & Bed & **0.56** & 0.55 \\ Fire Hydrant & **0.81** & 0.81 & Dining Table & **0.48** & 0.48 \\ Bench & 0.48 & **0.49** & Toilet & **0.79** & 0.78 \\ Bird & **0.55** & 0.55 & TV & **0.73** & 0.73 \\ Person & **0.82** & 0.81 & Bear & 0.58 & **0.58** \\ Car & **0.61** & 0.60 & Zebra & 0.84 & **0.84** \\ Motorcycle & 0.80 & **0.80** & Giraffe & 0.78 & **0.79** \\ Airplane & 0.65 & **0.66** & Umbrella & 0.69 & **0.69** \\ Bus & **0.69** & 0.69 & Bowl & **0.38** & 0.38 \\ Train & 0.69 & **0.70** & Pizza & 0.68 & **0.68** \\ Truck & 0.54 & **0.55** & Chair & **0.35** & 0.35 \\ Boat & 0.54 & **0.55** & Couch & 0.46 & **0.47** \\ Traffic Light & 0.53 & **0.54** & Bed & **0.56** & 0.55 \\ Fire Hydrant & **0.81** & 0.81 & Dining Table & **0.48** & 0.48 \\ Bench & 0.48 & **0.49** & Toilet & **0.79** & 0.78 \\ Bird & **0.55** & 0.55 & TV & **0.73** & 0.73 \\ Cat & **0.49** & 0.48 & Laptop & 0.66 & **0.67** \\ Dog & **0.67** & 0.66 & Oven & 0.53 & **0.56** \\ Horse & 0.69 & **0.70** & Sink & 0.53 & **0.54** \\ Sheep & 0.68 & **0.68** & Refrigerator & 0.60 & **0.61** \\ Cow & 0.58 & **0.58** & Clock & **0.67** & 0.66 \\ Elephant & 0.75 & **0.75** & Teddy Bear & **0.73** & 0.73 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study results of gradient weighting. The bold font number indicate the highest value corresponding to each label.