CHASD is an inference-time framework that gates contrastive decoding via an uncertainty threshold and constructs negative branches through attention-guided perturbations of salient visual tokens to mitigate hallucinations in LVLMs.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
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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CHASD: Language Increment-Calibrated Contrastive Decoding against Hallucination in LVLMs
CHASD is an inference-time framework that gates contrastive decoding via an uncertainty threshold and constructs negative branches through attention-guided perturbations of salient visual tokens to mitigate hallucinations in LVLMs.
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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.