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Unconstrained Salient Object Detection via Proposal Subset Optimization

CVPR 2016 · Detection and segmentation › CNN features · approach: RGB-D salient object detection · 109 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use MAP subset optimization instead of greedy NMSSalCNN · MSOAP: .654 -> .734Helped
Use MAP subset optimization instead of greedy NMSSalCNN · DUT-OAP: .432 -> .510Helped
Use MAP subset optimization instead of greedy NMSSalCNN · MSRAAP: .722 -> .778Helped
Use MAP subset optimization instead of greedy NMSSalCNN · VOC07AP: .300 -> .337Helped
Add adaptive region sampling (ablation)SalCNN · MSO overallAP: .504 -> .734Helped
Add adaptive region sampling (ablation)SalCNN · MSO single-object imagesAP: .676 -> .818Helped
Replace adaptive with uniform region sampling (ablation)SalCNN · MSO large-object imagesAP: .859 -> .726Hurt
Replace adaptive with uniform region sampling (ablation)SalCNN · MSO multiple-object imagesAP: .698 -> .540Helped
Use MAP filtering instead of score thresholding (ablation)SalCNN · MSO overallAP: .654 -> .734Helped
Use MAP filtering instead of MMR (ablation)SalCNN · MSO overallAP: .656 -> .734Helped
Use MAP filtering instead of rank thresholding (ablation)SalCNN · MSO single-object imagesAP: .717 -> .818Helped
Use MAP filtering instead of score thresholding (ablation)SalCNN · MSO large-object imagesAP: .833 -> .859Helped

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