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Bi-Directional Object-Context Prioritization Learning for Saliency Ranking

CVPR 2022 · Detection and segmentation › CNN features · approach: RGB salient object detection · 33 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add SOS and OCOR modules (ablation)Saliency ranking model · ASSRSOR: 0.837 -> 0.904Helped
Add SOS (ablation)Saliency ranking model · ASSRSOR: 0.885 -> 0.904Helped
Add OCOR (ablation)Saliency ranking model · ASSRSOR: 0.870 -> 0.904Helped
Add GCP alone (ablation)SOS baseline · ASSRSOR: 0.837 -> 0.840Helped
Add DR alone (ablation)SOS baseline · ASSRSOR: 0.837 -> 0.857Helped
Add GCP and DR together (ablation)SOS baseline · ASSRSOR: 0.837 -> 0.870Helped
Use concatenation and two convolutional layers for object-context relations (S1) (ablation)OCOR baseline · ASSRSOR: 0.837 -> 0.842Helped
Add channel-wise attention to S1 (S2) (ablation)OCOR baseline · ASSRSOR: 0.842 -> 0.845Helped
Add spatial-wise attention to S1 (S3) (ablation)OCOR baseline · ASSRSOR: 0.842 -> 0.853Helped
Add channel-wise and spatial-wise attention to S1 (S4) (ablation)OCOR baseline · ASSRSOR: 0.842 -> 0.857Helped
Replace alternative relation strategy with the proposed OCOR module (ablation)Object-context relation model · ASSRSOR: 0.857 -> 0.885Helped
Use bi-directional object-context prioritization learningSaliency ranking · ASSR and IRSR Test SetsSOR: ASSR: 0.904; IRSR: 0.834Helped

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