English

Retrieval Visual Contrastive Decoding to Mitigate Object Hallucinations in Large Vision-Language Models

Computer Vision and Pattern Recognition 2025-05-30 v2 Artificial Intelligence Machine Learning

Abstract

Despite significant advancements in Large Vision-Language Models, Object Hallucination (OH) remains a persistent challenge. Building upon prior studies on contrastive decoding that address this issue without requiring additional model training, we introduce RVCD (Retrieval Visual Contrastive Decoding), an advanced method to suppress OH. RVCD leverages both negative and positive images at the logit level, explicitly referencing AI-generated images designed to represent a single concept. Our approach demonstrates substantial improvements over existing decoding-based methods.

Keywords

Cite

@article{arxiv.2505.20569,
  title  = {Retrieval Visual Contrastive Decoding to Mitigate Object Hallucinations in Large Vision-Language Models},
  author = {Jihoon Lee and Min Song},
  journal= {arXiv preprint arXiv:2505.20569},
  year   = {2025}
}

Comments

ACL 2025 Findings camera-ready version. Code is released at https://github.com/JiHoonLee9898/RVCD

R2 v1 2026-07-01T02:41:17.788Z