OpenVAM: Open-World Visual Attention Modeling with VLMs
Predicting human gaze is a core capability for applications ranging from web/UI design analysis to robotics and human-computer interaction. Yet, most visual attention modeling methods output only a dense saliency map, which is often insufficient for action: practitioners need to connect attention peaks to discrete elements in the scene (what) and understand the drivers of those peaks in context (why), while remaining robust to domain shift across natural images, commercial content, and UI/web layouts. We, therefore, introduce OpenVAM (Open-world Visual Attention Modeling with VLMs), a unified framework that jointly addresses universality and explainability across heterogeneous domains (na...
| Authors | Kiana Hooshanfar, Amirhossein Kazerouni, Alireza Hosseini et al. |
|---|---|
| Primary category | cs.CV |
| Published | 25 September 2026 |
| Last updated at source | 25 September 2026 |
Models and datasets referencing this paper
- K-Hooshanfar/OpenVAMDataset
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