2026
Human Uncertainty-Aware Data Selection and Automatic Labeling in Visual Question Answering
ICLR 2026poster
Large vision-language models (VLMs) achieve strong performance in Visual Question Answering but still rely heavily on supervised fine-tuning (SFT) with massive labeled datasets, which is costly due to human annotations. Crucially, real-world datasets often exhibit *human uncertainty* (**HU**) — var…