STEAR reduces spatial and temporal hallucinations in Video-LLMs via layer-aware evidence intervention from middle decoder layers in a single-encode pass.
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A training-free region-aware attention recalibration strategy reduces object hallucinations in LVLMs on CHAIR, POPE, and MME benchmarks while preserving fluency.
citing papers explorer
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STEAR: Layer-Aware Spatiotemporal Evidence Intervention for Hallucination Mitigation in Video Large Language Models
STEAR reduces spatial and temporal hallucinations in Video-LLMs via layer-aware evidence intervention from middle decoder layers in a single-encode pass.
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Mitigating Object Hallucinations in Vision-Language Models through Region-Aware Attention Recalibration
A training-free region-aware attention recalibration strategy reduces object hallucinations in LVLMs on CHAIR, POPE, and MME benchmarks while preserving fluency.