arXiv (OAI Expanded)
open access
Human-Grounded Calibration for Long-Text Image-Text Congruence in Vision-Language Models
Long-text image--text congruence scoring is increasingly important for vision-language systems that must evaluate whether detailed textual descriptions match visual content. However, raw similarity scores from dual-encoder models are difficult to interpret as calibrated congruence measures, especial
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
- Characterizing Text Branch Sensitivity in Medical Vision-Language Segmentation via Evidence Decoupling#987839
- Imag-Eval: a language-grounded framework for interpretable Text-to-Image instruction following evaluation#794023
- Imag-Eval: a language-grounded framework for interpretable Text-to-Image instruction following evaluation#790004
- C-TPT: Calibrated Test-Time Prompt Tuning for Vision-Language Models via Text Feature Dispersion#436431
- Symbal: Detecting Systematic Misalignments in Model-Generated Captions#373421
Record · ID 1109621
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