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We introduce a variety of models, trained on a supervised image captioning corpus to predict the image features for a given caption, to perform sentence representation grounding.
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A SICK cure for the evaluation of compositional distributional semantic models
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Marco Baroni. 2016 · 2016
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Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
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Sequence to sequence learning with neural networks
I. Sutskever, O. Vinyals, and QV. Le. 2014 · 2014
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
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Mind’s eye: A recurrent visual representation for image caption generation
Xinlei Chen and Lawrence C Zitnick. 2015 · 2015
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Learning language through pictures
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Learning visual features from large weakly supervised data
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Learning visual n-grams from web data
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Generating images from captions with attention
Elman Mansimov, Emilio Parisotto, Jimmy Lei Ba, and Ruslan Salakhutdinov. 2016 · 2016
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Learning deep representations of fine-grained visual descriptions
S. Reed, Z. Akata, H. Lee, and B. Schiele. 2016 · 2016
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Conditional image generation with pixelcnn decoders
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Towards universal paraphrastic sentence embeddings
John Wieting, Mohit Bansal, Kevin Gimpel, and Karen Livescu. 2016 · 2016
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Visually grounded and textual semantic models differentially decode brain activity associated with concrete and abstract nouns
Andrew J. Anderson, Douwe Kiela, Stephen Clark, and Massimo Poesio. 2017 · 2017
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Learning cooperative visual dialog agents with deep reinforcement learning
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Deep embodiment: grounding semantics in perceptual modalities (PhD thesis)
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Emergent translation in multi-agent communication
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