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Research map / Detection and segmentation / CNN features

A Simple Pooling-Based Design for Real-Time Salient Object Detection

CVPR 2019 · Detection and segmentation › CNN features · approach: RGB-D salient object detection · 1,150 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add PPM and GGFs to FPN (ablation)VGG-16 FPN · DUT-OMaxF: 0.770 -> 0.790Helped
Add PPM and GGFs to FPN (ablation)VGG-16 FPN · SODMaxF: 0.838 -> 0.855Helped
Add FAMs to FPN (ablation)VGG-16 FPN · DUT-OMaxF: 0.770 -> 0.798Helped
Add FAMs to FPN (ablation)VGG-16 FPN · SODMaxF: 0.838 -> 0.852Helped
Add PPM to the GGM without GGFs (ablation)VGG-16 FPN · DUT-OMaxF: 0.790 -> 0.783Hurt
Add PPM to the GGM without GGFs (ablation)VGG-16 FPN · SODMaxF: 0.855 -> 0.847Hurt
Add GGFs to the GGM without PPM (ablation)VGG-16 FPN · DUT-OMaxF: 0.790 -> 0.772Hurt
Add GGFs to the GGM without PPM (ablation)VGG-16 FPN · SODMaxF: 0.855 -> 0.843Hurt
Jointly train using salient-object boundaries as edge supervision (ablation)PoolNet with edge branch · PASCAL-SMaxF: 0.838 -> 0.835Hurt
Jointly train using salient-object boundaries as edge supervision (ablation)PoolNet with edge branch · DUT-OMaxF: 0.806 -> 0.805Hurt
Jointly train using salient-object boundaries as edge supervision (ablation)PoolNet with edge branch · SODMaxF: 0.861 -> 0.863Helped
Jointly train with standard edge datasets (ablation)PoolNet with edge branch · PASCAL-SMaxF: 0.838 -> 0.849Helped

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