SCoOP: Semantic Consistent Opinion Pooling for Uncertainty Quantification in Multiple Vision-Language Model Systems
SCoOP: Semantic Consistent Opinion Pooling for Uncertainty Quantification in Multiple Vision-Language Model Systems explores A training-free framework to quantify uncertainty and detect hallucinations in multi-Vision-Language Model systems, improving reliability with minimal overhead.. Commercial viability score: 7/10 in Multimodal AI.
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