Good, Better, Best: Textual Distractors Generation for Multiple-Choice Visual Question Answering via Reinforcement Learning. (arXiv:1910.09134v3 [cs.CV] UPDATED)

Multiple-choice VQA has drawn increasing attention from researchers and
end-users recently. As the demand for automatically constructing large-scale
multiple-choice VQA data grows, we introduce a novel task called textual
Distractors Generation for VQA (DG-VQA) focusing on generating challenging yet
meaningful distractors given the context image, question, and correct answer.
The DG-VQA task aims at generating distractors without ground-truth training
samples since such resources are rarely available. To tackle the DG-VQA
unsupervisedly, we propose Gobbet, a reinforcement learning(RL) based framework
that …

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