The UNPG method, which focuses on unified negative pair generation for face recognition, can be extended to other computer vision tasks by adapting the concept of generating informative pairs to improve model training. In tasks such as object detection, image segmentation, and image classification, the idea of selecting negative pairs strategically to enhance the discriminative power of the model can be beneficial. For example, in object detection, negative pairs could be generated to emphasize the differences between objects and background regions, leading to more accurate localization. Similarly, in image segmentation, selecting negative pairs that challenge the model's ability to distinguish between different classes can improve segmentation accuracy. By incorporating the UNPG approach into these tasks, models can be trained more effectively by focusing on informative pairs that drive better feature learning and decision-making.