arXiv (OAI Expanded)
open access
SnapBench: Benchmarking Snap-and-Ask Multimodal Retrieval for Mobile Interactions
Mobile AI acts as a visual oracle, empowering users to snap a picture of something and ask for information. Snap-and-ask retrieval is now one of the most common entry points for mobile AI, yet photos are often blurry, while text questions may be short or mistyped. Existing benchmarks only test on cl
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
- SnapBench: Benchmarking Snap-and-Ask Multimodal Retrieval for Mobile Interactions#798247
- Artificial Intelligence for Pathology Diagnosis and Prognosis Prediction of Lung Nodule Using Smartphone Photos#1151310
- DocAttriBench: Benchmarking Answer Grounding in Document Visual Question Answering#1034387
- GenPuzzle: Benchmarking Visual Reasoning in Image Generation Models#973521
- VKnowU: Evaluating Visual Knowledge Understanding in Multimodal LLMs#925861
Record · ID 796834
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