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Label Decoupling Framework for Salient Object Detection

CVPR 2020 · Detection and segmentation › CNN features · approach: RGB salient object detection · 384 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use label decoupling and feature interaction in LDF rather than the strongest listed baselineLDF · ECSSDMAE: .035 -> .034Helped
Use LDF rather than the strongest listed baselineLDF · PASCAL-SmF: .832 -> .848Helped
Use LDF rather than the strongest listed baselineLDF · DUTS-TEmF: .815 -> .855Helped
Use LDF rather than the strongest listed baselineLDF · HKU-ISMAE: .032 -> .027Helped
Use LDF rather than the strongest listed baselineLDF · DUT-OMRONmF: .756 -> .773Helped
Use LDF rather than the strongest listed baselineLDF · THUR15KmF: .741 -> .764Helped
Add one feature interaction rather than none (ablation)LDF · THUR15KmF: 0.751 -> 0.764Helped
Add one feature interaction rather than none (ablation)LDF · DUTS-TEmF: 0.839 -> 0.855Helped
Use two or three interactions instead of one (ablation)LDF · THUR15K and DUTS-TEmF: 0.764 -> 0.756; 0.764 -> 0.753; 0.855 -> 0.849; 0.855 -> 0.842Hurt
Use body and detail supervision instead of body and edge supervision (ablation)LDF · THUR15K and DUTS-TEmF: 0.758 -> 0.764; 0.850 -> 0.855Helped
Use body and detail supervision instead of saliency and detail supervision (ablation)LDF · THUR15K and DUTS-TEmF: 0.756 -> 0.764; 0.848 -> 0.855Helped

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