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Research map / Detection and segmentation / CNN features

Cascaded Partial Decoder for Fast and Accurate Salient Object Detection

CVPR 2019 · Detection and segmentation › CNN features · approach: RGB-D salient object detection · 1,148 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use CPD instead of VGG16-based state-of-the-art modelsSalient object detection · ECSSDmaxF: 0.931 -> 0.936Helped
Use CPD instead of the strongest listed baselineSalient object detection · DUT-OMRONmaxF: 0.803 -> 0.794Hurt
Use CPD instead of PiCANet-RSalient object detection · DUT-OMRONFPS: 5 -> 66Helped
Add holistic attention rather than using initial attention (ablation)CPD · DUTSavgF: 0.803 -> 0.813Helped
Add holistic attention rather than using initial attention (ablation)CPD · DUTSMAE: 0.045 -> 0.043Helped
Use Conv2_2 instead of Conv3_3 as the optimization layer (ablation)CPD · ECSSDFPS: 66 -> 38Hurt
Use Conv4_3 instead of Conv3_3 as the optimization layer (ablation)CPD · ECSSDmaxF: 0.936 -> 0.931Hurt
Remove the optimization layer and integrate all-level features (ablation)CPD · ECSSDmaxF: 0.936 -> 0.922Hurt
Apply the cascaded partial decoder framework to BMPMBMPM · DUTSmaxF: 0.850 -> 0.870Helped
Apply the cascaded partial decoder framework to NLDFNLDF · DUTSmaxF: 0.830 -> 0.842Helped
Apply the cascaded partial decoder framework to AmuletAmulet · DUTSmaxF: 0.832 -> 0.845Helped
Use CPD for portrait segmentation instead of BMPMPortrait segmentation · Portrait segmentationMean IoU: 96.20% -> 96.60%Helped

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