✨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
📝 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
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#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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#LVLMs #VisualGrounding #AIHallucinations #ComputerVision #DeepLearning
📝 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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#LVLMs #VisualGrounding #AIHallucinations #ComputerVision #DeepLearning
✨GroundingME: Exposing the Visual Grounding Gap in MLLMs through Multi-Dimensional Evaluation
📝 Summary:
GroundingME is a new benchmark revealing significant visual grounding gaps in MLLMs, which often hallucinate instead of rejecting ungroundable queries. State-of-the-art models only reach 45.1% accuracy, raising safety concerns. Data-mixture training shows promise in improving their ability to rec...
🔹 Publication Date: Published on Dec 19
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.17495
• PDF: https://arxiv.org/pdf/2512.17495
• Project Page: https://groundingme.github.io/
==================================
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#MLLMs #VisualGrounding #AISafety #AIResearch #Benchmarking
📝 Summary:
GroundingME is a new benchmark revealing significant visual grounding gaps in MLLMs, which often hallucinate instead of rejecting ungroundable queries. State-of-the-art models only reach 45.1% accuracy, raising safety concerns. Data-mixture training shows promise in improving their ability to rec...
🔹 Publication Date: Published on Dec 19
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.17495
• PDF: https://arxiv.org/pdf/2512.17495
• Project Page: https://groundingme.github.io/
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#MLLMs #VisualGrounding #AISafety #AIResearch #Benchmarking
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