A new attention-enhancement method using ARS scores and RVE reduces action-relation hallucinations in LVLMs while generalizing to spatial and object hallucinations.
arXiv preprint arXiv:2406.09121 , year=
3 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 3years
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UNVERDICTED 3representative citing papers
Fox detects risky attention heads in LVLMs using visual attention entropy and severs hallucination shortcuts via numerical logit saturation and conflict-gated decoding, outperforming prior methods by 29.1%.
Mild rotations and noise significantly increase relation hallucinations in VLMs across models and datasets, with prompt and preprocessing fixes providing only partial relief.
citing papers explorer
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Mitigating Action-Relation Hallucinations in LVLMs via Relation-aware Visual Enhancement
A new attention-enhancement method using ARS scores and RVE reduces action-relation hallucinations in LVLMs while generalizing to spatial and object hallucinations.
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Dismantling Pathological Shortcuts: A Causal Framework for Faithful LVLM Decoding
Fox detects risky attention heads in LVLMs using visual attention entropy and severs hallucination shortcuts via numerical logit saturation and conflict-gated decoding, outperforming prior methods by 29.1%.
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When Relations Break: Analyzing Relation Hallucination in Vision-Language Model Under Rotation and Noise
Mild rotations and noise significantly increase relation hallucinations in VLMs across models and datasets, with prompt and preprocessing fixes providing only partial relief.