ED-CCF projects detections into a quad-state error taxonomy and applies class-conditional calibration only when empirically justified, raising mAP50 for a hard class by 22.4% on a 600-image benchmark while keeping global mAP50 stable.
In: 2025 19th International Conference on Semantic Computing (ICSC) (2025)
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Error-Decomposed Class-Conditional Fusion for Statistically Guaranteed Hard-Category Robust Perception
ED-CCF projects detections into a quad-state error taxonomy and applies class-conditional calibration only when empirically justified, raising mAP50 for a hard class by 22.4% on a 600-image benchmark while keeping global mAP50 stable.