arXiv (All)
open access
Revealing Multi-View Hallucination in Large Vision-Language Models
Large vision-language models (LVLMs) are increasingly being applied to multi-view image inputs captured from diverse viewpoints. Despite this growing use, current LVLMs often generate incorrect responses due to visual interference from non-target instances or viewpoints, a phenomenon we term multi-v
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
- Beneath the Scores: Rethinking Hallucination Evaluation for Video Understanding Models#1076647
- Partition-Aware Unlearning for Removing Spurious Correlations in Large Vision-Language Models#799242
- Partition-Aware Unlearning for Removing Spurious Correlations in Large Vision-Language Models#797819
- SpanCalib-VLM: Calibrated Hallucination Span Detection in Vision-Language Models#799221
- SpanCalib-VLM: Calibrated Hallucination Span Detection in Vision-Language Models#797798
Record · ID 820396
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