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Visual question answering (Visual QA) has attracted a lot of attention lately, seen essentially as a form of (visual) Turing test that artificial intelligence should strive to achieve.
Verbs semantics and lexical selection
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Natural language processing with Python: analyzing text with the natural language toolkit
Steven Bird, Ewan Klein, and Edward Loper. 2009 · 2009
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Every picture tells a story: Generating sentences from images
Ali Farhadi, Mohsen Hejrati, Mohammad Amin Sadeghi, Peter Young, Cyrus Rashtchian, Julia Hockenmaier, and David Forsyth. 2010 · 2010
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Im2text: Describing images using 1 million captioned photographs
Vicente Ordonez, Girish Kulkarni, and Tamara L Berg. 2011 · 2011
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Framing image description as a ranking task: Data, models and evaluation metrics
Micah Hodosh, Peter Young, and Julia Hockenmaier. 2013 · 2013
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
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Distributed representations of sentences and documents
Quoc V Le and Tomas Mikolov. 2014 · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. 2014 · 2014
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A multi-world approach to question answering about real-world scenes based on uncertain input
Mateusz Malinowski and Mario Fritz. 2014 · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014 · 2014
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Vqa: Visual question answering
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. 2015 · 2015
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Microsoft coco captions: Data collection and evaluation server
Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollár, and C Lawrence Zitnick. 2015 · 2015
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Exploring models and data for image question answering
Mengye Ren, Ryan Kiros, and Richard Zemel. 2015 · 2015
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ImageNet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei. 2015 · 2015
Cited alongside, same era.
Show, attend and tell: Neural image caption generation with visual attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron C Courville, Ruslan Salakhutdinov, Richard S Zemel, and Yoshua Bengio. 2015 · 2015
Cited alongside, same era.
Visual madlibs: Fill in the blank description generation and question answering
Licheng Yu, Eunbyung Park, Alexander C Berg, and Tamara L Berg. 2015 · 2015
Cited alongside, same era.
Analyzing the behavior of visual question answering models
Aishwarya Agrawal, Dhruv Batra, and Devi Parikh. 2016 · 2016
Cited alongside, same era.
Spice: Semantic propositional image caption evaluation
Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould. 2016 · 2016
Cited alongside, same era.
Ask, attend and answer: Exploring question-guided spatial attention for visual question answering
Huijuan Xu and Kate Saenko. 2016 · 2016
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Yin and yang: Balancing and answering binary visual questions
Peng Zhang, Yash Goyal, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh. 2016 · 2016
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Visual7w: Grounded question answering in images
Yuke Zhu, Oliver Groth, Michael Bernstein, and Li Fei-Fei. 2016 · 2016
Later among the works it cites.
Visual dialog
Abhishek Das, Satwik Kottur, Khushi Gupta, Avi Singh, Deshraj Yadav, José MF Moura, Devi Parikh, and Dhruv Batra. 2017 · 2017
Closest in time.
Making the v in vqa matter: Elevating the role of image understanding in visual question answering
Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh. 2017 · 2017
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Nan Ding, Sebastian Goodman, Fei Sha, and Radu Soricut. 2016 · 2016
Cited alongside, same era.
Multimodal compact bilinear pooling for visual question answering and visual grounding
Akira Fukui, Dong Huk Park, Daylen Yang, Anna Rohrbach, Trevor Darrell, and Marcus Rohrbach. 2016 · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
Cited alongside, same era.
Focused evaluation for image description with binary forced-choice tasks
Micah Hodosh and Julia Hockenmaier. 2016 · 2016
Cited alongside, same era.
Revisiting visual question answering baselines
Allan Jabri, Armand Joulin, and Laurens van der Maaten. 2016 · 2016
Cited alongside, same era.
Leveraging visual question answering for image-caption ranking
Xiao Lin and Devi Parikh. 2016 · 2016
Cited alongside, same era.
How not to evaluate your dialogue system: An empirical study of unsupervised evaluation metrics for dialogue response generation
Chia-Wei Liu, Ryan Lowe, Iulian V Serban, Michael Noseworthy, Laurent Charlin, and Joelle Pineau. 2016 · 2016
Cited alongside, same era.
Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick. 2017 · 2017
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Re-evaluating automatic metrics for image captioning
Mert Kilickaya, Aykut Erdem, Nazli Ikizler-Cinbis, and Erkut Erdem. 2017 · 2017
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Visual genome: Connecting language and vision using crowdsourced dense image annotations
Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma, Michael Bernstein, and Li Fei-Fei. 2017 · 2017
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Active learning for visual question answering: An empirical study
Xiao Lin and Devi Parikh. 2017 · 2017
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Learned in translation: Contextualized word vectors
Bryan McCann, James Bradbury, Caiming Xiong, and Richard Socher. 2017 · 2017
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Visual question answering: A survey of methods and datasets
Qi Wu, Damien Teney, Peng Wang, Chunhua Shen, Anthony Dick, and Anton van den Hengel. 2017 · 2017
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Don’t just assume; look and answer: Overcoming priors for visual question answering
Aishwarya Agrawal, Dhruv Batra, Devi Parikh, and Aniruddha Kembhavi. 2018 · 2018
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