arXiv (All)
open access
Metric-Driven Reinforcement Learning for Object Hallucination Reduction in LVLM-Based Image Captioning
Large Vision-Language Models (LVLMs) have excelled in joint visual and language understanding, particularly in image captioning. However, LVLMs remain prone to object hallucination, describing non-existent entities in the given image. Previous remedies, including dataset-enhanced supervised training
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
- Reducing Hallucination in Multimodal Large Language Models through Hard Grounding Preference Supervision#1116282
- Human-Centric Image Captioning with Subject-Centered Spatial Understanding#985668
- Mitigating Hallucination in Large Vision-Language Models via Adaptive Attention Calibration#1343417
- Does Playing it Safe Count as Faithfulness? Reassessing LVLM Hallucination Mitigation Methods#822307
- Fine-Grained Multi Image Object Hallucination Benchmark#818709
Record · ID 1325872
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