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Adaptive Early-Learning Correction for Segmentation From Noisy Annotations

CVPR 2022 · Detection and segmentation › Weakly supervised detection and segmentation · approach: Weakly supervised semantic segmentation · 117 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use adaptive, class-wise label correction with multiscale consistencyUNet trained on noisy SegTHOR annotations · SegTHOR testmIoU: 63.3$\pm$2.0 -> 71.2$\pm$0.6Helped
Correct labels for all classes at the same time instead of class-adaptively (ablation)ADELE on SegTHOR · SegTHOR testmIoU: 71.2$\pm$0.6 -> 40.7$\pm$2.4Hurt
Apply ADELE to SEAM-generated annotationsSEAM / ResNet-38 · PASCAL VOC 2012 valmIoU: 64.5 -> 69.3Helped
Apply ADELE to ICD-generated annotationsICD / ResNet-38 · PASCAL VOC 2012 testmIoU: 65.5 -> 68.9Helped
Add multiscale input augmentation (ablation)SegTHOR segmentation baseline · SegTHOR valmIoU: 58.8 -> 60.7Helped
Add multiscale consistency regularization (ablation)SegTHOR segmentation baseline with multiscale inputs · SegTHOR valmIoU: 60.7 -> 62.5Helped
Add adaptive label correction (ablation)SegTHOR single-scale baseline · SegTHOR valmIoU: 58.8 -> 65.2Helped
Add adaptive label correction and multiscale consistency rather than no correction with multiscale consistency (ablation)SegTHOR · SegTHOR valmIoU: 62.5 -> 72.2Helped
Add multiscale consistency regularization (ablation)PASCAL VOC 2012 segmentation baseline with multiscale inputs · PASCAL VOC 2012 valmIoU: 65.5 -> 66.7Helped
Add adaptive label correction (ablation)PASCAL VOC 2012 single-scale baseline · PASCAL VOC 2012 valmIoU: 64.5 -> 65.6Helped
Add adaptive label correction and multiscale consistency rather than no correction with multiscale consistency (ablation)PASCAL VOC 2012 · PASCAL VOC 2012 valmIoU: 66.7 -> 69.3Helped
Use per-iteration rather than epoch-end model outputs for label correction (ablation)ADELE on SegTHOR · SegTHOR testmIoU: 70.2 -> 71.2Helped

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