arXiv (All)
open access
Interpretable Modeling of Driver Attention Shifts with a Vision-Language Model
Driver gaze is commonly modeled as a spatial heatmap, but heatmaps alone are difficult for humans to interpret because they do not explain which road object or region is being monitored or why an attention shift may matter. This study examines whether minimal human-grounded supervision can steer a v
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
- GIFT: Goal-Injected Fine-Tuning for Efficient Manipulation Policy Adaptation#975075
- The Mirage of Calibrated Confidence: Trajectory-Independence of Verbalized Confidence in Vision-Language Models#1012638
- Search-based Testing of Vision Language Models for In-Car Scene Understanding#332586
- Aligning Vision and Language: Harnessing Language Semantics for Efficient Vision Models#995758
- Visual Attention Faithfulness in Vision-Language Models is Heterogeneous#820835
Record · ID 820229
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