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Question answering is an important task for autonomous agents and virtual assistants alike and was shown to support the disabled in efficiently navigating an overwhelming environment.
Long short-term memory
Hochreiter, S., Schmidhuber, J.: · 1997
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Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars
Zettlemoyer, L.S., M.Collins: · 2005
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Learning context-dependent mappings from sentences to logical form
Zettlemoyer, L.S., M.Collins: · 2005
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Large Margin Methods for Structured and Interdependent Output Variables
Tsochantaridis, I., Joachims, T., Hofmann, T., Altun, Y.: · 2005
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Dbpedia: A nucleus for a web of open data
Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: · 2007
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: · 2009
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A survey on question answering technology from an information retrieval perspective
Kolomiyets, O., Moens, M.F.: · 2011
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Template-based question answering over RDF data
Unger, C., Bühmann, L., Lehmann, J., Ngomo, A.C.N., Gerber, D., Cimiano, P.: · 2012
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Semantic Parsing on Freebase from Question-Answer Pairs
Berant, J., Chou, A., Frostig, R., Liang, P.: · 2013
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Large-scale Semantic Parsing via Schema Matching and Lexicon Extension
Cai, Q., Yates, A.: · 2013
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Learning dependency-based compositional semantics
Liang, P., Jordan, M.I., Klein, D.: · 2013
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Scaling semantic parsers with on-the-fly ontology matching
Kwiatkowski, T., Choi, E., Artzi, Y., Zettlemoyer, L.: · 2013
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Jointly learning to parse and perceive: Connecting natural language to the physical world
Krishnamurthy, J., Kollar, T.: · 2013
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Towards a visual turing challenge
Malinowski, M., Fritz, M.: · 2014
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A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input
Malinowski, M., Fritz, M.: · 2014
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Webchild: Harvesting and organizing commonsense knowledge from the web
Tandon, N., de Melo, G., Suchanek, F., Weikum, G.: · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2014
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Semantic parsing via paraphrasing
Berant, J., Liang, P.: · 2014
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Open question answering over curated and extracted knowledge bases
Fader, A., Zettlemoyer, L., Etzioni, O.: · 2014
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Information extraction over structured data: Question answering with Freebase
Yao, X., Durme, B.V.: · 2014
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Question answering with sub-graph embeddings
Bordes, A., Chopra, S., Weston, J.: · 2014
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Open question answering with weakly supervised embedding models
Bordes, A., Weston, J., Usunier, N.: · 2014
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Glove: Global vectors for word representation
Pennington, J., Socher, R., Manning, C.D.: · 2014
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Microsoft coco: Common objects in context
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C. Lawrence, e.D., Pajdla, T., Schiele, B., Tuytelaars, T.: · 2014
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2d human pose estimation: New benchmark and state of the art analysis
Andriluka, M., Pishchulin, L., Gehler, P., Schiele, B.: · 2014
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Exploring models and data for image question answering
Ren, M., Kiros, R., Zemel, R.: · 2015
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Visual Madlibs: Fill in the blank image generation and question answering
Yu, L., Park, E., Berg, A., Berg, T.: · 2015
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VQA: Visual Question Answering
Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C.L., Parikh, D.: · 2015
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Are you talking to a machine? Dataset and Methods for Multilingual Image Question Answering
Gao, H., Mao, J., Zhou, J., Huang, Z., Wang, L., Xu, W.: · 2015
Cited alongside, same era.
Ask your neurons: A neural-based approach to answering questions about images
Malinowski, M., Rohrbach, M., Fritz, M.: · 2015
Dynamic memory networks for visual and textual question answering
Xiong, C., Merity, S., Socher, R.: · 2016
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Multimodal residual learning for visual qa
Kim, J.H., Kwak, S.W.L.D.H., Heo, M.O., Kim, J., Ha, J.W., Zhang, B.T.: · 2016
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Measuring machine intelligence through visual question answering
Zitnick, C.L., Agrawal, A., Antol, S., Mitchell, M., Batra, D., Parikh, D.: · 2016
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Wu, Q., Shen, C., van den Hengel, A., Wang, P., Dick, A.: · 2016
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Transforming dependency structures to logical forms for semantic parsing
Reddy, S., Täckström, O., Collins, M., Kwiatkowski, T., Das, D., Steedman, M., Lapata, M.: · 2016
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Simple baseline for visual question answering
Zhou, B., Tian, Y., Sukhbataar, S., Szlam, A., Fergus, R.: · 2015
Cited alongside, same era.
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: · 2015
Cited alongside, same era.
Semantic parsing via staged query graph generation: Question answering with knowledge base
Yih, W., Chang, M.W., He, X., Gao, J.: · 2015
Cited alongside, same era.
Question answering over freebase with multi-column convolutional neural networks
Dong, L., Wei, F., Zhou, M., Xu, K.: · 2015
Cited alongside, same era.
Large-scale simple question answering with memory networks
Bordes, A., Usunier, N., Chopra, S., Weston, J.: · 2015
Cited alongside, same era.
Building a large-scale multimodal Knowledge Base for Visual Question Answering
Zhu, Y., Zhang, C., Ré, C., Fei-Fei, L.: · 2015
Cited alongside, same era.
Sequence-based structured prediction for semantic parsing
Xiao, C., Dymetman, M., Gardent, C.: · 2016
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Ask Me Anything: Free-form Visual Question Answering Based on Knowledge from External Sources
Wu, Q., Wang, P., Shen, C., van den Hengel, A., Dick, A.: · 2016
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Improving information extraction by acquiring external evidence with reinforcement learning
Narasimhan, K., Yala, A., Barzilay, R.: · 2016
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Ask me anything: Free-form visual question answering based on knowledge from external sources
Wu, Q., Wang, P., Shen, C., Dick, A., van den Hengel, A.: · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
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Learning models for actions and person-object interactions with transfer to question answering
Mallya, A., Lazebnik, S.: · 2016
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Visual genome: Connecting language and vision using crowdsourced dense image annotations
Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.J., Shamma, D.A., et al.: · 2017
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Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Johnson, J., Hariharan, B., van der Maaten, L., Fei-Fei, L., Zitnick, C.L., Girshick, R.: · 2017
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Making the v in vqa matter: Elevating the role of image understanding in visual question answering
Goyal, Y., Khot, T., Summers-Stay, D., Batra, D., Parikh, D.: · 2017
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Learning to reason: End-to-end module networks for visual question answering
Hu, R., Andreas, J., Rohrbach, M., Darrell, T., Saenko, K.: · 2017
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Conceptnet 5.5: An open multilingual graph of general knowledge
Speer, R., Chin, J., Havasi, C.: · 2017
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High-Order Attention Models for Visual Question Answering
Schwartz, I., Schwing, A.G., Hazan, T.: · 2017
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Mutan: Multimodal tucker fusion for visual question answering
Ben-younes, H., Cadene, R., Cord, M., Thome, N.: · 2017
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Creativity: Generating Diverse Questions using Variational Autoencoders
Jain, U., Zhang, Z., Schwing, A.G.: · 2017
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Explicit Knowledge-based Reasoning for Visual Question Answering
Wang, P., Wu, Q., Shen, C., van den Hengel, A., Dick, A.: · 2017
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Explicit knowledge-based reasoning for visual question answering
Wang, P., Wu, Q., Shen, C., Dick, A., Van Den Henge, A.: · 2017
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Places: A 10 million image database for scene recognition
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A.: · 2017
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Fvqa: Fact-based visual question answering
Wang, P., Wu, Q., Shen, C., Dick, A., v. d. Hengel, A.: · 2018
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Two can play this Game: Visual Dialog with Discriminative Question Generation and Answering
Jain, U., Lazebnik, S., Schwing, A.G.: · 2018
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