arXiv (All)
open access
SAGE: Semantic Attribute Graphs for Multi-Entity Visual Retrieval
Dense document images often contain many fine-grained visual and textual entities whose relevance depends on a user query. Standard vision-language retrievers encode cropped regions with a single vector, which can mix distinct entity signals and obscure the evidence needed for fine-grained retrieval
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
- ARK: A Dual-Axis Multimodal Retrieval Benchmark along Reasoning and Knowledge#1141241
- Concept hierarchy across languages in text-based image retrieval: a user evaluation#279053
- Correcting to Predict: Pseudo-Value Correction for Multimodal Attribute Value Extraction#1128655
- Blending Concepts: Benchmarking Visual Metaphor Generation in Text-to-Image Models#632878
- Fine-Grained Multi Image Object Hallucination Benchmark#818709
Record · ID 927555
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