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🧠 AI⚪ NeutralImportance 6/10
Revealing Multi-View Hallucination in Large Vision-Language Models
arXiv – CS AI|Wooje Park, Insu Lee, Soohyun Kim, Jaeyun Jang, Minyoung Noh, Kyuhong Shim, Byonghyo Shim|
🤖AI Summary
Researchers identify 'multi-view hallucination' as a major problem in large vision-language models (LVLMs), where these AI systems confuse visual information from different viewpoints or instances. They created MVH-Bench benchmark and developed Reference Shift Contrastive Decoding (RSCD) technique, which improved performance by up to 34.6 points without requiring model retraining.
Key Takeaways
- →Large vision-language models suffer from multi-view hallucination, confusing visual information from different viewpoints or instances.
- →MVH-Bench benchmark with 4.8k question-answer pairs was created to systematically test cross-instance and cross-view hallucination.
- →Current LVLMs including Qwen2.5-VL and LLaVA-OneVision struggle to correctly associate visual evidence with corresponding instances.
- →Reference Shift Contrastive Decoding (RSCD) is a training-free technique that suppresses visual interference through attention masking.
- →RSCD improved performance by up to 34.6 points over existing hallucination mitigation methods without requiring model retraining.
#vision-language-models#hallucination#multi-view#benchmark#decoding-technique#computer-vision#ai-research#model-performance
Read Original →via arXiv – CS AI
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