UniReason-Med introduces a unified framework for 2D and 3D medical VQA with shared grounded reasoning, trained on a 220K dataset, claiming that joint 2D+3D supervision improves 3D performance over 3D-only training.
arXiv preprint arXiv:2107.03134 , year=
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Links MLLM hallucinations to attention distraction and introduces AFIP to correct it via cross-head enrichment and dynamic historical attention without retraining.
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UniReason-Med: A Shared Grounded Reasoning Interface for 2D-to-3D Transfer in Medical VQA
UniReason-Med introduces a unified framework for 2D and 3D medical VQA with shared grounded reasoning, trained on a 220K dataset, claiming that joint 2D+3D supervision improves 3D performance over 3D-only training.
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Correcting Visual Blur Induced by Attention Distraction to Reduce Hallucinations: Algorithm and Theory
Links MLLM hallucinations to attention distraction and introduces AFIP to correct it via cross-head enrichment and dynamic historical attention without retraining.