# Seg-Eigen-CAM: Eigen-Value-Based Visual Explanations for Semantic Segmentation Models
## 2 Related Work  
### Overview of Traditional XAI Methods in Image Classification  
#### 2.1.1 Grad-CAM  
Grad-CAM is a generalized form of CAM that is not only applicable to any CNN architecture but is also capable of generating saliency maps for any target label without architectural changes or retraining. Since it remains a CAM-based approach, Grad-CAM computes saliency maps through a weighted linear combination of feature maps (typically from the last convolutional layer), where the weights are derived from the global average pooling (GAP) of gradients. Specifically, Grad-CAM utilizes gradients and feature maps to generate the class-discriminative localization map \(L_{\text{Grad-CAM}}^{c}\), which is defined as follows:  
\[L_{\text{Grad-CAM}}^{c}=\text{ReLU}\left(\sum_{k}a_{k}^{c}\cdot A^{k}\right) \tag{1}\]  
where \(A^{k}\) denotes the \(k\)-th feature map, \(a_{k}^{c}\) denotes the corresponding weight for the \(k\)-th feature map, and \(c\) denotes the target class. The ReLU function is applied to retain only the positive contributions in the computation. In classification tasks, the weight is obtained by backpropagating the gradient of the target class score with respect to the feature map. Specifically, the weight for each feature map is computed as the normalized sum of its corresponding gradient matrix, defined as follows:  
\[a_{k}^{c}=\text{GAP}(\frac{\partial Y^{c}}{\partial A^{k}}) \tag{2}\]  
Grad-CAM is widely used to explain deep learning model decisions by generating a heatmap that intuitively highlights the regions of an input image to which the model pays attention. However, its application in certain scenarios presents inherent limitations and challenges. Specifically, when applied to image segmentation models, Grad-CAM encounters a fundamental problem, as shown in Figure 1. Unlike image classification models, which predict a single class for the entire image, segmentation models produce pixel-level predictions, requiring each pixel to be assigned a class label. This characteristic causes the model's gradient information to simultaneously reflect multiple class-related features. When using gradient-based methods like Grad-CAM to interpret the attention regions of a specific target class, the presence of gradients corresponding to unrelated classes can introduce interference. Consequently, the generated heatmap may prove insufficient in precisely highlighting the target objects. In contrast, classification models assign a single label to the entire image, which means that their gradient information primarily corresponds to the most probable class, reducing interference from other categories. Moreover, when processing images that contain complex backgrounds or multiple objects of the same category, the strategy of using global average pooling values as weights can lead to the loss of spatial information. As a result, the localization outcome often fails to fully cover the entire object. In such cases, the Grad-CAM heatmap may struggle to accurately distinguish or comprehensively cover each target object. These issues can reduce the correlation between explanation results and the decision-making process of the model, thereby affecting the model's interpretability and practical applicability.