Abstract:Methods for teaching machines to answer visual questions have made significant progress in the last few years, but although demonstrating impressive results on particular datasets, these methods lack some important human capabilities, including integrating new visual classes and concepts in a modular manner, providing explanations for the answer and handling new domains without new examples. In this paper we present a system that achieves state-of-the-art results on the CLEVR dataset without any questions-answ… Show more
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