# Seg-Eigen-CAM: Eigen-Value-Based Visual Explanations for Semantic Segmentation Models
## 4 Experiments
### Case Study and Limitation Analysis  
To further explore the potential limitations of evaluation metrics, this study selected several cases in which our method performed relatively poorly in quantitative metrics within images containing the target class (person). These cases help uncover biases and misconceptions that may arise from relying solely on quantitative metrics for evaluation. As shown in Figure 10, in the first two scenes, although our method achieved relatively low Dice scores, the visual results reveal that Seg-Grad-CAM and Seg-XRes-CAM, despite achieving higher Dice scores, retained a larger retention of the image area. Specifically, Seg-XRes-CAM produced visual results similar to ours but retained a large amount of low-intensity responses in the background. This suggests that higher Dice scores may not necessarily reflect the quality of the explanation accurately. In the third scene, our method retained only 3% of the image area while still achieving a Dice score of 0.87. This further corroborates the fact that low-intensity responses generated by gradient-based methods may originate from gradient noise rather than key factors influencing the decision-making process of the model. Furthermore, in the fourth scene, our method not only identified the crowd in the stands but also accurately captured the head region of the players on the field. This aligns with the experimental conclusion in Section 4.4.3, showing that the model's attention is typically focused on the head region of the target objects. This characteristic is consistent with the Human Visual System, indicating that the explanations generated by our method are more intuitive and interpretable. However, these cases also highlight the limitations of relying solely on quantitative metrics for evaluation. When the pixel area of the target class is extremely small, even if the explanation effectively reflects the model's decision-making process and aligns with human intuition, the limited features in that area may cause prediction failures when the masked image is re-predicted, leading to inaccurate conclusions from the evaluation metrics. Although evaluation metrics can assess the overall performance of XAI methods and provide insight into the characteristics of the method through 2D histograms, the black-box nature of deep learning models makes it difficult to accurately distinguish between errors in the model itself and biases in the attribution method. Therefore, relying solely on quantitative metrics to evaluate the quality of the explanation, especially in individual cases, can lead to erroneous conclusions. To further investigate the explanation characteristics of different methods, we performed analyses in three cases with different scene characteristics, as shown in Figure 11. The experimental results demonstrate that our method exhibits a unique attention distribution pattern. Specifically, in the first scene, our method accurately captures the model's focus on facial regions; in the second scene, it effectively highlights the hair regions; and in the third scene, it clearly delineates the subject's edge contours. In contrast, while other methods achieved higher scores in evaluation metrics, their explanation contained substantial background regions that may be irrelevant to the decision-making process. This observation raises the crucial question of whether high evaluation metric scores truly reflect the actual effectiveness of explanation methods. Overly complex or coarse explanations may fail to accurately represent the model's decision-making process, and it remains challenging to verify whether these non-target regions substantially influence the model's decisions. Although our method generates explanations that align more closely with human visual perception and offer better interpretability, the inherent black-box nature of deep learning models prevents us from fully validating the faithfulness of these explanations. Figure 11: Analysis of limitations of XAI methods. Figure 10: Analysis of the limitations of evaluation metrics.