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Research map / Detection and segmentation / Weakly supervised detection and segmentation

Label Matching Semi-Supervised Object Detection

CVPR 2022 · Detection and segmentation › Weakly supervised detection and segmentation · approach: Pseudo-labeling for semi-supervised detection · 93 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace a single fixed confidence threshold with adaptive label-distribution-aware thresholds (ablation)Mean-teacher pseudo labeling · COCO-standard 1% (ablation setting)AP50:95Helped
Stop updating ACT during training (ablation)LabelMatch · COCO-standard 1% (ablation setting)AP50:95Hurt
Use online rather than offline ACT (ablation)LabelMatch · COCO-standardAP50:95: 24.6 vs 24.6; 31.6 vs 31.5; 34.6 vs 34.6No clear effect
Set all candidate pseudo labels as uncertain instead of separating reliable and uncertain labels (ablation)LabelMatch · COCO-standard 1% (ablation setting)AP50:95Hurt
Set too many pseudo labels as reliable (ablation)LabelMatch · COCO-standard 1% (ablation setting)AP50:95Hurt
Use proposal self-assignment for uncertain pseudo labels instead of ignore assignment or IoU-based assignment (ablation)LabelMatch · COCO-standard 1% (ablation setting)AP50:95Helped
Add reliable pseudo label mining to convert high-quality uncertain labels into reliable labels (ablation)LabelMatch · COCO-standardAP50:95Helped
Use RPLM thresholds (0.8, 0.8) rather than other tested threshold pairs (ablation)LabelMatch · COCO-standard 10%AP50:95: 34.4; 34.5; 34.6; 34.3Mixed
Use ACT rather than a fixed threshold in STAC (ablation)STAC · COCO-standard 1%AP50:95Helped
Estimate the unlabeled class distribution using its ground-truth labels rather than the labeled source distribution (ablation)LabelMatch · Small→LargeAP50: 38.8 to 44.5Helped

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