Journal
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017)
Volume -, Issue -, Pages 6146-6155Publisher
IEEE
DOI: 10.1109/CVPR.2017.651
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Funding
- ONR MURI award
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Humans possess an extraordinary ability to learn new skills and new knowledge for problem solving. Such learning ability is also required by an automatic model to deal with arbitrary, open-ended questions in the visual world. We propose a neural-based approach to acquiring task-driven information for visual question answering (VQA). Our model proposes queries to actively acquire relevant information from external auxiliary data. Supporting evidence from either human-curated or automatic sources is encoded and stored into a memory bank. We show that acquiring task-driven evidence effectively improves model performance on both the Visual7W and VQA datasets; moreover, these queries offer certain level of interpretability in our iterative QA model.
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