# Seg-Eigen-CAM: Eigen-Value-Based Visual Explanations for Semantic Segmentation Models
## 1 Introduction
After applying gradient weighting to the activation maps, the method progressively aggregates them into a comprehensive saliency map, allowing the heatmap to intuitively display the regions of the image that the model focuses on. This improvement laid a crucial foundation for subsequent research and spurred a series of advances. Among these, [PERSON] et al. [12] introduced Grad-CAM++, which refines the weight calculation by considering the second derivative of each pixel's contribution to the classification score, improving localization accuracy in multi-instance scenarios. [PERSON] et al. [13] proposed Layer-CAM, which incorporates shallow convolutional feature maps and fuses multilayer feature maps to generate more granular visualizations of saliency maps. Meanwhile, [PERSON] et al. [14] introduced XGrad-CAM, which combines an expected gradient approach to calculate the expected gradient values of the output predictions with respect to the activation maps, thus reducing the impact of gradient fluctuations on the saliency maps. However, some derivative methods argue that using gradient information to compute linear coefficients is unreliable and propose alternative approaches. For example, [PERSON] et al. [15] introduced Eigen-CAM, which applies Principal Component Analysis (PCA) to feature weighting based on the activation maps. [PERSON] et al. [16] developed Score-CAM, which determines weights by calculating the contribution of each activation map to the predicted class score. [PERSON] [17] proposed Ablation-CAM, which uses a feature masking strategy to iteratively assess the impact of each feature map on model predictions, determining their importance. The evolution of these methods reflects the transition from simple gradient calculations to more complex mathematical models, continually enhancing the ability to handle multiple-instance scenarios and fine-grained details, thus producing more stable and reliable explanations. Although CAM-based methods have achieved significant results, most of these techniques were originally developed for image classification tasks and are limited to providing image-level explanations. These approaches typically generate coarse heatmaps to highlight the most influential regions for a single predicted class. Object detection networks, which require both classification and localization capabilities, represent an intermediate level of granularity between classification and segmentation tasks. Although detection methods can benefit from region-level CAM explanations to understand bounding-box predictions, they still do not require the fine-grained pixel-level interpretability demanded by semantic segmentation. Segmentation tasks present unique challenges, as they require dense and pixel-accurate explanations that preserve spatial relationships and boundary details. Consequently, directly applying conventional CAM methods to segmentation models often leads to spatial detail loss and reduced localization accuracy. Therefore, the development of explainability methods that can be applied to a broader range of visual tasks remains an important research direction in this field. Recently, researchers have begun to propose specialized XAI methods for image segmentation tasks. For example, Seg-Grad-CAM, introduced by [PERSON] et al. [18], extends the Grad-CAM framework to interpret segmentation models, generating saliency maps that indicate the relevance of individual pixels or regions through masking. Furthermore, Seg-XRes-CAM, presented by [PERSON] et al. [19], improves this approach by combining feature maps with residual connections and fine-grained spatial information, further enhancing the accuracy of segmentation region explanations. As emphasized by [PERSON] et al. [20], the retention of spatial information is crucial to accurately reflecting the decision-making process of pixel-level localization models. This can be achieved by improving the weight computation and avoiding the effects of global pooling.