# Seg-Eigen-CAM: Eigen-Value-Based Visual Explanations for Semantic Segmentation Models
## 5 Conclusions and Future Work  
In this study, we have proposed an improved interpretability method for convolutional neural networks in semantic segmentation tasks. Building upon the Eigen-CAM framework, our approach incorporates spatial information and class-relevance scores from gradients, resulting in weighted activation maps that better capture region-specific attention. Furthermore, we introduced a dynamic sign correction mechanism to optimize feature representations derived from the singular value decomposition of weighted activation maps, effectively addressing the sign ambiguity issues that commonly arise in multi-class segmentation scenarios.  
Our experimental results, comparing against established interpretability methods including Eigen-CAM, Seg-Grad-CAM, and Seg-XRes-CAM, demonstrate several significant findings. Although our method may not consistently outperform Seg-XRes-CAM in individual quantitative metrics, it exhibits superior performance in the Preserved Effectiveness metric. This high PE score substantiates that our method effectively identifies the critical regions necessary for class recognition without relying on complete target pixels or extensive background information. The method generates refined local explanations that align more closely with human visual perception, effectively highlighting object contours and key regions while filtering out low-relevance areas.  
However, through a detailed qualitative analysis, we identified certain limitations in traditional evaluation metrics. Although our highly refined explanations may occasionally lead to prediction failures during re-prediction tests, they effectively reflect the model's decision-making process. The visual results, while similar to Seg-XRes-CAM in some aspects, provide users with more concise and intuitive visual explanations, which is the core objective of interpretability methods.  
Looking ahead, several promising research directions emerge from our findings. First, while our current dynamic sign correction mechanism effectively handles sign ambiguity, there is room for improvement in complex scenes. Future research could explore the integration of adaptive feature weight adjustment with multiscale feature analysis to enhance interpretation accuracy across varying target scales and scene complexities. Second, the method's applicability could be extended to broader domains, such as medical image analysis and remote sensing, to evaluate its generalization capabilities comprehensively. Third, investigating the extensibility of the method to other deep learning architectures, including instance segmentation and object detection models, would validate its versatility.  
A particularly crucial area for future work lies in developing more comprehensive evaluation frameworks. Given that the ultimate goal of interpretability methods is to provide intuitive and meaningful explanations to users, there is a need to establish human-centric evaluation standards. These standards should effectively assess how well the explanations aid human understanding and align with human visual cognition patterns. Such evaluation frameworks should combine both quantitative metrics and qualitative analyses to provide a more complete assessment of interpretability methods.  
Conceptualization, J.J.-C.Y.; methodology, C.-T.C.; Writing--original draft preparation, C.-T.C.; Writing--review and editing, J.J.-C.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported (in part) by NSTC 113-2634-F-005-002--project Smart Sustainable New Agriculture Research Center (SMARTer).  
Not applicable.  
Data Availability Statement: The data presented in this study are available from the corresponding author on request. The data are not publicly available due to personal information protection used in the study.  
The authors declare no conflict of interest.