Evaluate the selection principle on tasks beyond object detection

Determine whether linking a target model’s internal representations to prediction errors and their expected effects on the evaluation metric is effective for image classification, segmentation, sequence prediction, or other learning tasks with definable error types and metric effects.

Background

The method was evaluated primarily with YOLOv8n for object detection on COCO 2017 and BDD100K, with a restricted Faster R-CNN comparison. The authors explain that applying the principle to other tasks would require task-specific internal representations, error categories, and estimates of each error type’s effect on the evaluation metric. Its effectiveness outside object detection was not resolved by the reported experiments.

References

Its effectiveness on other tasks remains to be tested.

— From internal representations to model improvement through prediction errors  (2609.35449 - Nakaya et al., 28 Sep 2026) in Discussion, paragraph beginning “The proposed selection principle is not inherently specific to object detection”