Research map / Vision-language and multimodal / Human and embodied modeling
Learning From Unique Perspectives: User-Aware Saliency Modeling
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add personalized user filters and a user mask to EML-Net | EML-Net · FiWI | NSS: 1.653 -> 1.752 | Helped |
| Add personalized user filters and a user mask to EML-Net | EML-Net · OSIE | NSS: 1.737 -> 1.840 | Helped |
| Add progressive learning of attention composition (ablation) | User-aware saliency model · FiWI, K=1 | NSS: 1.908 -> 2.059 | Helped |
| Add progressive learning of attention composition (ablation) | User-aware saliency model · OSIE, K=5 | CC: 0.625 -> 0.629 | Helped |
| Train with personalized user preferences and progressive learning | User-aware saliency model · FiWI, missing users, K=5 | NSS: 1.712 -> 1.797 | Helped |
Closest papers by meaning
- UniAR: A Unified model for predicting human Attention and Responses on visual content (NeurIPS 2024 · similarity 0.90)
- What Do Deep Saliency Models Learn about Visual Attention? (NeurIPS 2023 · similarity 0.90)
- A Deeper Look at Saliency: Feature Contrast, Semantics, and Beyond (CVPR 2016 · similarity 0.89)
- CaRDiff: Video Salient Object Ranking Chain of Thought Reasoning for Saliency Prediction with Diffusion (AAAI 2025 · similarity 0.87)
- A Motion-aware Spatio-temporal Graph for Video Salient Object Ranking (NeurIPS 2024 · similarity 0.86)
- Explainable Saliency: Articulating Reasoning with Contextual Prioritization (CVPR 2025 · similarity 0.85)
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