Retrieval Visual Contrastive Decoding to Mitigate Object Hallucinations in Large Vision-Language Models
About
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.
Jihoon Lee, Min Song• 2025
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Visual Hallucination Evaluation | POPE MS-COCO Adversarial sampling (val) | Accuracy85.48 | 39 | |
| Object Hallucination Evaluation | MSCOCO | Accuracy88.54 | 21 | |
| Image Captioning | MSCOCO (random sampling of 500 instances) | CHAIRs11.32 | 21 | |
| Vision-Language Evaluation | MME Leaderboard (full) | Existence Score130 | 18 |
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