The quad-metric boundary error estimation proposed in the context of instance segmentation can be extended to other computer vision tasks by adapting the error metrics to suit the specific requirements of each task.
For object detection, the quad-metric boundary error can be modified to focus on the accuracy of bounding box predictions. The error estimation can include metrics such as intersection over union (IoU) for object localization, false positives, false negatives, true positives, and true negatives at the object boundaries. By incorporating these metrics, the model can better understand the quality of object detection and refine the predictions accordingly.
In the case of semantic segmentation, the quad-metric boundary error can be adjusted to evaluate pixel-wise classification errors. This can involve metrics like pixel-wise accuracy, precision, recall, and F1 score at the boundaries of semantic regions. By analyzing these errors, the model can improve the segmentation results by focusing on misclassified pixels and refining the boundaries of semantic regions.
Overall, by customizing the quad-metric boundary error estimation for different computer vision tasks, it can provide valuable insights into the specific challenges faced by each task and guide the refinement process towards more accurate and reliable results.