arXiv (All)
open access
VisExMEM: Visual Evidence-Based Explainable Multimodal Evaluation Metric
Current image-text evaluation often relies on a global score, which gives limited insight into which semantic details are supported by the image. Explainable metrics provide rationales, but the visual evidence supporting those rationales can still be difficult to verify. We introduce VisExMEM, a ref
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
- When Does an Image Determine the Answer? Benchmarking Visual Answerability across Charts and Scenes#1128749
- Rigel: Self-Distilled Score Adaptation for Image and Video Captioning Evaluation#1109090
- Learning Multimodal Embeddings with Evidence-Aligned Readout#1118732
- 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
Record · ID 1328031
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