Variational Visual Question Answering for Uncertainty-Aware Selective Prediction

• URL: http://arxiv.org/abs/2505.09591v2
• Date: Fri, 31 Oct 2025 02:57:26 GMT
• Title: Variational Visual Question Answering for Uncertainty-Aware Selective Prediction
• Authors: Tobias Jan Wieczorek, Nathalie Daun, Mohammad Emtiyaz Khan, Marcus Rohrbach,
• Abstract summary: We show the effectiveness and competitive edge of variational Bayes for selective prediction in Visual Question Answering (VQA) for the first time.<n>This method improves calibration and yields significant gains for selective prediction on VQA and Visual Reasoning.<n>In addition, we propose a new risk-averse selector that outperforms standard sample averaging by considering the variance of predictions.
• Score: 17.353372524615363
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite remarkable progress in recent years, vision language models (VLMs) remain prone to overconfidence and hallucinations on tasks such as Visual Question Answering (VQA) and Visual Reasoning. Bayesian methods can potentially improve reliability by helping models selectively predict, that is, models respond only when they are sufficiently confident. Unfortunately, Bayesian methods are often assumed to be costly and ineffective for large models, and so far there exists little evidence to show otherwise, especially for multimodal applications. Here, we show the effectiveness and competitive edge of variational Bayes for selective prediction in VQA for the first time. We build on recent advances in variational methods for deep learning and propose an extension called "Variational VQA". This method improves calibration and yields significant gains for selective prediction on VQA and Visual Reasoning, particularly when the error tolerance is low ($\leq 1\%$). Often, just one posterior sample can yield more reliable answers than those obtained by models trained with AdamW. In addition, we propose a new risk-averse selector that outperforms standard sample averaging by considering the variance of predictions. Overall, we present compelling evidence that variational learning is a viable option to make large VLMs safer and more trustworthy.

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