# Seg-Eigen-CAM: Eigen-Value-Based Visual Explanations for Semantic Segmentation Models
## 1 Introduction  
With the advancement of deep learning, particularly convolutional neural networks (CNNs), a breakthrough was achieved in 2012 at the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) [1]. Since then, CNNs have been widely adopted in various computer vision tasks, including image classification, object detection, and image segmentation. The subsequent introduction of architectures such as VGGNet [2], GoogLeNet [3], and ResNet [4] led to significant success in image classification. However, as convolutional neural networks become more and more complex, their multilayered and diverse architectures have led to black-box characteristics in the internal operation of the model. Therefore, the explainability of the model becomes more and more important. To address this issue, Explainable Artificial Intelligence (XAI) methods have been developed to improve the transparency and explainability of deep learning models to help researchers and users better understand the decision-making process of the model. To solve the problem of black-box properties of deep learning models, researchers have proposed various XAI methods, which can be broadly classified into two categories: (a) non-Class Activation Mapping (non-CAM) methods and (b) Class Activation Mapping (CAM) and its derivatives. In non-CAM methods, researchers [5; 6; 7; 8; 9] have explored different techniques to interpret the decision-making processes of deep learning models. For instance, [PERSON] et al. [7] introduced Deconvolutional Networks, which reverse convolutional layer operations to project activations back to the input space, revealing the model's response patterns. [PERSON] et al. [8] proposed Guided Backpropagation, which refines feature visualization by combining forward propagation with gradient-based backpropagation. Meanwhile, [PERSON] et al. [9] developed Integrated Gradients, a mathematically grounded approach that quantifies the importance of features using linear interpolation and integration, generating saliency maps. Despite their advantages, these methods exhibit several limitations. They often involve high computational complexity and instability in the interpretation results, particularly in deep networks, where vanishing or exploding gradients can degrade the quality of the explanation. Furthermore, their implementation frequently requires setting critical parameters such as baselines and reference points. The lack of standardized selection criteria for these parameters makes the interpretability results highly dependent on expert heuristics, compromising reproducibility and reliability. These challenges hinder the practical applicability of non-CAM methods, limiting their effectiveness in real-world scenarios. To overcome the aforementioned limitations and provide more stable and intuitive explanation results, research focus has gradually shifted towards methods based on the theory of CAM. CAM-based methods focus on directly visualizing the decision-making rationale behind a model's predictions in visual interpretability tasks. CAM proposed by [PERSON] et al. [10] is a pioneering work in this field, which generates saliency maps for specific categories by mapping the model's decision outcomes back to the input image. This breakthrough is not only technically significant but also demonstrates significant value in real-world applications, such as medical imaging diagnosis, enabling professionals with nontechnical backgrounds to understand the decision basis of the model while providing model developers with an effective tool for model bias identification and performance optimization. To address this issue, [PERSON] et al. [11] proposed Grad-CAM, which enhances CAM by weighting feature maps from specific convolutional layers. The method performs gradient backpropagation on each convolutional layer's feature map to obtain the weights of each feature.