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The task of Outside Knowledge Visual Question Answering (OKVQA) requires anautomatic system to answer natural language questions about pictures and imagesusing external knowledge. We observe that many visual questions, which containdeictic referential phrases referring to entities in the image, can berewritten as "non-grounded" questions and can be answered by existingtext-based question answering systems. This allows for the reuse of existingtext-based Open Domain Question Answering (QA) Systems for visual questionanswering. In this work, we propose a potentially data-efficient approach thatreuses existing systems for (a) image analysis, (b) question rewriting, and (c)text-based question answering to answer such visual questions. Given an imageand a question pertaining to that image (a visual question), we first extractthe entities present in the image using pre-trained object and sceneclassifiers. Using these detected entities, the visual questions can berewritten so as to be answerable by open domain QA systems. We explore tworewriting strategies: (1) an unsupervised method using BERT for masking andrewriting, and (2) a weakly supervised approach that combines adaptiverewriting and reinforcement learning techniques to use the implicit feedbackfrom the QA system. We test our strategies on the publicly available OKVQAdataset and obtain a competitive performance with state-of-the-art models whileusing only 10% of the training data.

Can Open Domain Question Answering Systems Answer Visual Knowledge Questions?