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Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation

AAAI 2021 · Detection and segmentation › Weakly supervised detection and segmentation · approach: CAM refinement and prototypes · 131 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use group-wise graph reasoning over image groupsGroup-WSSS · PASCAL VOC 2012 valmIoU: 67.8% -> 68.2%Helped
Use group-wise graph reasoning over image groupsGroup-WSSS · COCO valmIoU: 27.7% -> 28.4%Helped
Use K=3 images instead of K=4 (ablation)Group-WSSS · PASCAL VOC 2012 valmIoU: 68.2% -> 68.1%No clear effect
Use more than four images per group (K=5 or K=6) (ablation)Group-WSSS · PASCAL VOC 2012 valmIoU: 68.2% -> 67.8%; 68.2% -> 67.6%Hurt
Use T=2 message-passing steps instead of T=3 (ablation)Group-WSSS · PASCAL VOC 2012 valmIoU: 67.8% -> 68.2%Hurt
Use more than three message-passing steps (T=4 or T=5) (ablation)Group-WSSS · PASCAL VOC 2012 valmIoU: 68.2% -> 68.0%Hurt
Set graph-dropout δd to 0.9 or 0.5 instead of 0.7 (ablation)Group-WSSS · PASCAL VOC 2012 valmIoU: 68.2% -> 68.0%; 68.2% -> 67.7%Hurt
Lower graph-dropout δr to 0.6 or 0.4 (ablation)Group-WSSS · PASCAL VOC 2012 valmIoU: 68.2% -> 66.8%; 68.2% -> 63.6%Hurt
Add graph dropout to suppress highly activated regions (ablation)Group-WSSS · PASCAL VOC 2012 valmIoU: 67.7% -> 68.2%Helped
Use graph readout instead of intermediate readout only (ablation)Group-WSSS · PASCAL VOC 2012 valmIoU: 64.1% -> 67.8%Helped
Ensemble intermediate and graph outputs (ablation)Group-WSSS · PASCAL VOC 2012 valmIoU: 67.8% -> 68.2%Helped

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