# Seg-Eigen-CAM: Eigen-Value-Based Visual Explanations for Semantic Segmentation Models
## 2 Related Work
### Evaluation Strategies and Performance Metrics for XAI Methods  
In the study of explainability in convolutional neural networks, the establishment of an objective and standardized evaluation mechanism for XAI remains a critical challenge. Currently, there is no universally accepted set of evaluation metrics to assess the relevance, fidelity, and overall quality of visual explanations. Existing XAI evaluation strategies can be broadly categorized into two main approaches: (a) relevance-based element retention and (b) relevance-based element masking. In element retention evaluation, researchers preserve high-relevance regions highlighted by the saliency map and reinput them into the model to observe changes in classification confidence. If confidence increases or remains stable, it suggests that key features have been accurately identified. Conversely, in element masking evaluation, the highly relevant regions indicated by the saliency map are occluded before re-evaluating the model's predictions. Theoretically, if these occluded regions contain crucial features, confidence should drop significantly. Both evaluation methods have shown effectiveness in image classification models [28]. However, these evaluation techniques, originally designed for image classification, lead to novel challenges in the evaluation of image segmentation models. Classification models primarily rely on a single confidence score for evaluation, whereas segmentation models make pixel-level predictions, making assessment criteria more complex. To address this challenge, researchers have proposed various evaluation methods tailored for segmentation models [19; 29; 30; 31]. For example, [PERSON] et al. [19] introduced the Dice score as a benchmark, which can assess the effectiveness of saliency maps in two ways: (1) measuring the degree of overlap between the segmentation results obtained from masked inputs and the original input, and (2) evaluating the consistency of the segmentation results when only the most relevant regions are retained. A substantial decrease in the Dice score following the masking of salient regions suggests that these regions play a crucial role in the model decision-making process. Conversely, if the model maintains similar segmentation performance when restricted to only the salient regions, reflected in a consistently high Dice score, it further validates the significance of these regions in the prediction process. However, while the Dice score provides valuable insight, it remains a limited measure, as it does not fully capture the multifaceted nature of explanation quality in different evaluation dimensions. To address this limitation, [PERSON] et al. [30] proposed the Image Preserved Score as a complementary metric to quantify the proportion of the original image retained by the saliency map. By combining the Image Preserved Score with other metrics, a more holistic evaluation of whether an explanation method accurately identifies regions that significantly contribute to model predictions can be achieved. Recently, [PERSON] et al. [31] systematically reviewed and expanded XAI evaluation methods for image segmentation tasks. In addition to conventional evaluation approaches, masking salient regions (M1) and retaining salient regions (M2), they introduced an innovative metric (M3) based on Shannon entropy [32]. This metric integrates highlighted pixels and target-class pixels, assessing the effectiveness of an explanation method by quantifying the uncertainty of the model in target-class predictions. Specifically, if the model shows only a slight increase in the prediction entropy, it suggests that the XAI method has accurately identified the key regions that influence the model decision-making process. Although these methods offer diverse approaches to evaluating the quality of explanation in image segmentation models, relying solely on a single metric to assess the quality of explanation presents certain limitations.