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Research map / Detection and segmentation / CNN features

Progressive Attention Guided Recurrent Network for Salient Object Detection

CVPR 2018 · Detection and segmentation › CNN features · approach: RGB salient object detection · 696 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add progressive channel-wise and spatial attentionPAGRN · HKU-ISF-measureHelped
Add middle supervision to attentive features (ablation)PAGRN · THUR15KF-measure: 0.706 -> 0.716Helped
Add middle supervision to attentive features (ablation)PAGRN · DUTSF-measure: 0.750 -> 0.761Helped
Add recurrent connection at layer 5 (ablation)PAGRN · THUR15KF-measure: 0.716 -> 0.720Helped
Add recurrent connections at layers 4 and 5 (ablation)PAGRN · THUR15KF-measure: 0.716 -> 0.719Helped
Add recurrent connections at layers 3, 4, and 5 (ablation)PAGRN · THUR15KF-measure: 0.716 -> 0.729Helped
Add a recurrent connection at layer 2 to connections at layers 3, 4, and 5 (ablation)PAGRN · THUR15KF-measure: 0.729 -> 0.721Hurt
Add recurrent connections at layers 1 and 2 to connections at layers 3, 4, and 5 (ablation)PAGRN · THUR15KF-measure: 0.729 -> 0.727Hurt
Apply recurrent connections twice instead of once (ablation)PAGRN with connections at layers 3, 4, and 5 · THUR15KF-measure: 0.729 -> 0.723Hurt
Merge outputs from time steps rather than use the final time step (ablation)PAGRN with connections at layers 3, 4, and 5 · THUR15KF-measure: 0.729 -> 0.723Hurt
Apply recurrent connections twice and merge time-step outputs (ablation)PAGRN with connections at layers 3, 4, and 5 · THUR15KF-measure: 0.729 -> 0.716Hurt
Add recurrent connections at layers 3, 4, and 5 (ablation)PAGRN · DUTSF-measure: 0.761 -> 0.788Helped

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