  Visual question answering (VQA) has traditionally been treated as a
single-step task where each question receives the same amount of effort, unlike
natural human question-answering strategies. We explore a question
decomposition strategy for VQA to overcome this limitation. We probe the
ability of recently developed large vision-language models to use human-written
decompositions and produce their own decompositions of visual questions,
finding they are capable of learning both tasks from demonstrations alone.
However, we show that naive application of model-written decompositions can
hurt performance. We introduce a model-driven selective decomposition approach
for second-guessing predictions and correcting errors, and validate its
effectiveness on eight VQA tasks across three domains, showing consistent
improvements in accuracy, including improvements of >20% on medical VQA
datasets and boosting the zero-shot performance of BLIP-2 above chance on a VQA
reformulation of the challenging Winoground task. Project Site:
https://zaidkhan.me/decomposition-0shot-vqa/
