VAUQ: Vision-Aware Uncertainty Quantification for LVLM Self-Evaluation | ScienceToStartup | ScienceToStartup

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References (35)

[1]

Mitigating Hallucinations in Large Vision-Language Models by Self-Injecting Hallucinations

2025Yifan Lu, Ziqi Zhang et al.

[2]

GLSim: Detecting Object Hallucinations in LVLMs via Global-Local Similarity

2025Seongheon Park, Yixuan Li

[3]

InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency

Founder's Pitch

"VAUQ enhances vision-language models by providing a training-free, vision-aware uncertainty quantification framework for more reliable self-evaluation."

Vision-Language Models•Score: 5•View PDF ↗

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VAUQ: Vision-Aware Uncertainty Quantification for LVLM Self-Evaluation

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Founder's Pitch

"VAUQ enhances vision-language models by providing a training-free, vision-aware uncertainty quantification framework for more reliable self-evaluation."

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VAUQ: Vision-Aware Uncertainty Quantification for LVLM Self-Evaluation

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References (35)

Founder's Pitch

"VAUQ enhances vision-language models by providing a training-free, vision-aware uncertainty quantification framework for more reliable self-evaluation."

Commercial Viability Breakdown

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