Research map / Probabilistic and scientific modeling / Dynamical systems
Variational Laws of Visual Attention for Dynamic Scenes
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use exact brightness invariance term instead of upper-bound approximation (ablation) | EYMOL · MIT1003 | NSS: 1.2784 -> 1.2865 | No clear effect |
| use exact brightness invariance term instead of upper-bound approximation (ablation) | EYMOL · CAT2000-TRAIN | NSS: 1.8208 -> 1.8103 | No clear effect |
| use exact brightness invariance term instead of upper-bound approximation (ablation) | EYMOL · MIT1003 | AUC: 0.7996 -> 0.7990 | No clear effect |
Closest papers by meaning
- Dissecting the Interplay of Attention Paths in a Statistical Mechanics Theory of Transformers (NeurIPS 2024 · similarity 0.85)
- DiffEye: Diffusion-Based Continuous Eye-Tracking Data Generation Conditioned on Natural Images (NeurIPS 2025 · similarity 0.85)
- Unifying Top-down and Bottom-up Scanpath Prediction Using Transformers (CVPR 2024 · similarity 0.85)
- High-Dimensional Learning Dynamics of Attention-Indexed Models (NeurIPS 2026 · similarity 0.85)
- ST-DiffEye: Diffusion-based Continuous Gaze Generation via Joint Scanpath-Trajectory Modeling (NeurIPS 2026 · similarity 0.85)
- Attention (as Discrete-Time Markov) Chains (NeurIPS 2025 · similarity 0.84)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.