arXiv (All)
open access
GeoContext: One Context Ladder, Two Failure Modes in Vision-Language Geolocation: Flat Reliance on User-Provided Location Context and False Confirmation of Location Claims
Visual geolocation benchmarks typically ask a model where an image was captured without accounting for the location context that users often provide. We introduce GeoContext, a resource supporting two complementary tasks: GeoHint, open-ended localization given a true but coarse location hint, and Ge
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
- SCOPD: Sparse-Context On-Policy Self-Distillation for Efficient Vision-Language Models#1119117
- Where Am I? Semantic Map Grounding via Vision-Language Models for Multi-Modal Localization#1109093
- SpatialCORE: Confidence-Aware Grounded Spatial Reasoning in Large Vision--Language Models#1143317
- VisionPsy-Nano: Improving Accuracy, Efficiency, and Reliability in On-Device Vision-Language Models#1115702
- Towards Active Cross-View Object Geo-Localization#1014250
Record · ID 972805
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