Exploring Question Decomposition for Zero-Shot VQA

• URL: http://arxiv.org/abs/2310.17050v1
• Date: Wed, 25 Oct 2023 23:23:57 GMT
• Title: Exploring Question Decomposition for Zero-Shot VQA
• Authors: Zaid Khan, Vijay Kumar BG, Samuel Schulter, Manmohan Chandraker, Yun Fu
• Abstract summary: We investigate a question decomposition strategy for visual question answering. 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.
• Score: 99.32466439254821
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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/

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