The visualization method and threshold optimization techniques proposed in the paper can be extended to various other computer vision tasks beyond Face Anti-Spoofing (FAS). For instance, in object detection tasks, visualizing the model's predictions on different datasets can provide insights into how the model generalizes across different object classes or scenarios. By analyzing the data domain changes and setting thresholds based on the distribution of training data, models in object detection can be optimized for better performance. Additionally, in image segmentation tasks, visualizing the segmentation results and determining appropriate thresholds can help improve the accuracy of segmenting objects from backgrounds. The methodology can also be applied to tasks like image classification, where understanding the data domain variations and setting thresholds based on visualization analysis can enhance the model's classification capabilities.