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Diagrams are common tools for representing complex concepts, relationships and events, often when it would be difficult to portray the same information with natural images.
A schema for the study of graphic language (tutorial paper)
Twyman, M.: · 1979
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Computational models for integrating linguistic and visual information: A survey
Srihari, R.K.: · 1994
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Document Image Analysis
O’Gorman, L., Kasturi, R.: · 1997
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Long short-term memory
Hochreiter, S., Schmidhuber, J.: · 1997
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Telling juxtapositions: Using repetition and alignable difference in diagram understanding
Ferguson, R.W., Forbus, K.D.: · 1998
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Diagram understanding using integration of layout information and textual information
Watanabe, Y., Nagao, M.: · 1998
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Visual language: Global communication for the 21st century
Horn, R.: · 1998
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Readings in information visualization: using vision to think
Card, S.K., Mackinlay, J.D., Shneiderman, B.: · 1999
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The language of graphics: A framework for the analysis of syntax and meaning in maps, charts and diagrams
von Engelhardt, J.: · 2002
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Extraction, layout analysis and classification of diagrams in pdf documents
Futrelle, R.P., Shao, M., Cieslik, C., Grimes, A.E.: · 2003
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Image parsing: Unifying segmentation, detection, and recognition
Tu, Z., Chen, X., Yuille, A.L., Zhu, S.C.: · 2003
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A stochastic grammar of images
Zhu, S.C., Mumford, D.: · 2006
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Character recognition in natural images
de Campos, T.E., Babu, B.R., Varma, M.: · 2009
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Highly accurate boundary detection and grouping
Kokkinos, I.: · 2010
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Rectified linear units improve restricted boltzmann machines
Nair, V., Hinton, G.E.: · 2010
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Parsing natural scenes and natural language with recursive neural networks
Socher, R., Lin, C.C.Y., Ng, A.Y., Manning, C.D.: · 2011
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Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: · 2011
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Measuring the objectness of image windows
Alexe, B., Deselaers, T., Ferrari, V.: · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
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Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tieleman, T., Hinton, G.E.: · 2012
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Bringing semantics into focus using visual abstraction
Zitnick, C.L., Parikh, D.: · 2013
Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2014
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Glove: Global vectors for word representation
Pennington, J., Socher, R., Manning, C.D.: · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2014
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Yin and yang: Balancing and answering binary visual questions
Zhang, P., Goyal, Y., Summers-Stay, D., Batra, D., Parikh, D.: · 2015
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Learning common sense through visual abstraction
Vedantam, R., Lin, X., Batra, T., Zitnick, C.L., Parikh, D.: · 2015
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Bayesian grammar learning for inverse procedural modeling
Martinovic, A., Gool, L.J.V.: · 2013
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Understanding indoor scenes using 3d geometric phrases
Choi, W., Chao, Y.W., Pantofaru, C., Savarese, S.: · 2013
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Mctest: A challenge dataset for the open-domain machine comprehension of text
Richardson, M., Burges, C.J.C., Renshaw, E.: · 2013
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Selective search for object recognition
Uijlings, J.R.R., van de Sande, K.E.A., Gevers, T., Smeulders, A.W.M.: · 2013
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Diagram understanding in geometry questions
Seo, M.J., Hajishirzi, H., Farhadi, A., Etzioni, O.: · 2014
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Zero-shot learning via visual abstraction
Antol, S., Zitnick, C.L., Parikh, D.: · 2014
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Weston, J., Bordes, A., Chopra, S., Mikolov, T.: · 2015
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Teaching machines to read and comprehend
Hermann, K.M., Kocisky, T., Grefenstette, E., Espeholt, L., Kay, W., Suleyman, M., Blunsom, P.: · 2015
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End-to-end memory networks
Sukhbaatar, S., Weston, J., Fergus, R., et al.: · 2015
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Vqa: Visual question answering
Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Lawrence Zitnick, C., Parikh, D.: · 2015
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Exploring models and data for image question answering
Ren, M., Kiros, R., Zemel, R.: · 2015
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Visual7w: Grounded question answering in images
Zhu, Y., Groth, O., Bernstein, M.S., Fei-Fei, L.: · 2015
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Image question answering using convolutional neural network with dynamic parameter prediction
Noh, H., Seo, P.H., Han, B.: · 2015
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You only look once: Unified, real-time object detection
Redmon, J., Divvala, S.K., Girshick, R.B., Farhadi, A.: · 2015
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TensorFlow: Large-scale machine learning on heterogeneous systems (2015) Software available from tensorflow.org
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: · 2015
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Learning to compose neural networks for question answering
Andreas, J., Rohrbach, M., Darrell, T., Klein, D.: · 2016
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