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This paper presents stacked attention networks (SANs) that learn to answer natural language questions from images.
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S. Hochreiter and J. Schmidhuber · 1997
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Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Earlier work this paper cites.
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