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Human Uncertainty-Aware Data Selection and Automatic Labeling in Visual Question Answering

arXiv — cs.CV•Monday, November 3, 2025 at 5:00:00 AM
PositiveArtificial Intelligence

Human Uncertainty-Aware Data Selection and Automatic Labeling in Visual Question Answering

A recent study highlights advancements in Visual Question Answering (VQA) by addressing the challenges posed by human uncertainty in data labeling. Traditional methods rely heavily on large labeled datasets, which can be expensive and often overlook the variations in human confidence. This research proposes a new approach that not only improves the efficiency of data selection but also enhances the model's performance by incorporating these uncertainties. This is significant as it could lead to more robust AI systems that better understand and interpret human input, ultimately making VQA more accessible and effective.
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