✨Towards Mitigating Hallucinations in Large Vision-Language Models by Refining Textual Embeddings

📝 Summary:
Large Vision-Language Models suffer from language bias leading to hallucinations. Our method refines textual embeddings by integrating average-pooled visual features. This simple approach improves visual grounding and reduces hallucinations.

🔹 Publication Date: Published on Nov 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.05017
• PDF: https://arxiv.org/pdf/2511.05017

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#VisionLanguageModels #AIHallucinations #VisualGrounding #DeepLearning #NLP

✨Draft and Refine with Visual Experts

📝 Summary:
The Draft and Refine DnR framework improves visual grounding in LVLMs. It uses a novel question-conditioned utilization metric to measure visual evidence reliance. DnR refines responses with external visual experts, reducing hallucinations and boosting accuracy.

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11005
• PDF: https://arxiv.org/pdf/2511.11005
• Github: https://github.com/EavnJeong/Draft-and-Refine-with-Visual-Experts

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For more data science resources:
✓ https://t.iss.one/DataScienceT

#LVLMs #VisualGrounding #AIHallucinations #ComputerVision #DeepLearning