arXiv (All)
open access
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 pe
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- GazeVLM: Active Vision via Internal Attention Control for Multimodal Reasoning#1118555
- Air-Ground Collaborative Vision-and-Language Navigation via Shared Bird's-Eye Maps#985264
- Serious Game-Based Attention Assessment for Attention-Deficit/Hyperactivity Disorder in Children and Adolescents: Preliminary Cross-Sectional Convergent Validation Study.#785476
- TabSwift: An Efficient Tabular Foundation Model with Row-Wise Attention#266184
- Putting Attentional Control to a Normative Test#348882
Record · ID 1106063
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive