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An Integrated Model for Effective Saliency Prediction
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use maxima normalization instead of max pooling to fuse pathways (ablation) | SCA · Weighted Avg. over 5 datasets | sAUC: 0.7074 -> 0.7123 | Helped |
| use maxima normalization instead of average pooling to fuse pathways (ablation) | SCA · Weighted Avg. over 5 datasets | sAUC: 0.7116 -> 0.7123 | Helped |
| use maxima normalization instead of average pooling (ablation) | SCA · Cerf | sAUC: 0.798 -> 0.799 | Helped |
| add contrast-aware pathway and fusion to a semantic-only deep saliency network (SCA vs DPN) | SCA · Weighted Avg. over 5 datasets | sAUC: 0.6946 -> 0.7123 | Helped |
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- Predicting When Saliency Maps Are Accurate and Eye Fixations Consistent (CVPR 2016 · similarity 0.89)
- A Deeper Look at Saliency: Feature Contrast, Semantics, and Beyond (CVPR 2016 · similarity 0.88)
- Understanding and Visualizing Deep Visual Saliency Models (CVPR 2019 · similarity 0.87)
- Explainable Saliency: Articulating Reasoning with Contextual Prioritization (CVPR 2025 · similarity 0.85)
- CaRDiff: Video Salient Object Ranking Chain of Thought Reasoning for Saliency Prediction with Diffusion (AAAI 2025 · similarity 0.85)
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