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Progressive Feature Polishing Network for Salient Object Detection

AAAI 2020 · Detection and segmentation › CNN features · approach: RGB salient object detection · 107 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add progressive feature polishing with 2 FPMs (ablation)PFPN with ResNet-101 · ECSSDmax F: 0.928 -> 0.949Helped
Add progressive feature polishing with 2 FPMs (ablation)PFPN with ResNet-101 · DUTS-TEmax F: 0.851 -> 0.888Helped
Increase the FPM count from 2 to 3 (ablation)PFPN with ResNet-101 · ECSSDmax F: 0.949 -> 0.950Helped
Increase the FPM count from 2 to 3 (ablation)PFPN with ResNet-101 · DUTS-TEmax F: 0.888 -> 0.888No clear effect
Share weights between the two FPMs instead of using separate weights (ablation)PFPN with ResNet-101 · ECSSDmax F: 0.949 -> 0.944Hurt
Share weights between the two FPMs instead of using separate weights (ablation)PFPN with ResNet-101 · DUTS-TEmax F: 0.888 -> 0.876Hurt
Add DenseCRF post-processing (ablation)PFPN · DUTS-TEmax F: 0.888 -> 0.871Hurt
Use PFPN rather than the strongest reported ResNet baselineResNet-based salient object detection · ECSSDMAE: 0.036 -> 0.033Helped
Use PFPN rather than the strongest reported ResNet baselineResNet-based salient object detection · HKU-ISmax F: 0.928 -> 0.939Helped
Use PFPN rather than the strongest reported ResNet baselineResNet-based salient object detection · DUT-Omax F: 0.805 -> 0.820Helped
Use PFPN rather than the strongest reported ResNet baselineResNet-based salient object detection · DUTS-TEMAE: 0.043 -> 0.037Helped
Use PFPN rather than the strongest reported ResNet baselineResNet-based salient object detection · PASCAL-Smax F: 0.881 -> 0.892Helped

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