FUSE: Quantifying Uncertainty in Vision-Language Models by Bayesian Fusing Epistemic and Aleatoric Uncertainty
Vision-language models are crucial in robotics and other domains, but output uncertainty quantification remains essential. FUSE proposes a probabilistic framework combining aleatoric (data-ambiguity) and epistemic (model-diversity) uncertainties via Bayesian fusion into a scalar measure that predicts output correctness, achieving state-of-the-art calibration.
[2606.14728] FUSE: Quantifying Uncertainty in Vision-Language Models by Bayesian Fusing Epistemic and Aleatoric Uncertainty
[Submitted on 1 Jun 2026]
Title:FUSE: Quantifying Uncertainty in Vision-Language Models by Bayesian Fusing Epistemic and Aleatoric Uncertainty
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Abstract:Vision-language models (VLMs) are playing an increasingly important role across multiple domains. In many applications, such as robotics, it is crucial to quantify the uncertainty in the output of these models. } We develop FUSE, a probabilistic framework for capturing two complementary sources of uncertainty in vision-language modeling: (i) aleatoric embedding-level uncertainty derived from input data vision-language ambiguity, and (ii) epistemic model-level uncertainty estimated from the semantic response diversity of VLMs. Our approach formulates a Bayesian fusion mechanism that analytically combines these uncertainty sources to produce a scalar measure of uncertainty. This measure can be used to reliably predict the model's output correctness for downstream applications. We demonstrate that our method outperforms baselines and achieves SOTA uncertainty calibration.
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Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.14728 [cs.CV]
(or arXiv:2606.14728v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.14728
arXiv-issued DOI via DataCite
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From: Harry Zhang Mr. [view email] [v1] Mon, 1 Jun 2026 22:11:00 UTC (1,582 KB)
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