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A rich line of research attempts to make deep neural networks more transparent by generating human-interpretable 'explanations' of their decision process, especially for interactive tasks like Visual Question Answering (VQA).
Is seeing believing?: how recommender system interfaces affect users’ opinions
Dan Cosley, Shyong K Lam, Istvan Albert, Joseph A Konstan, and John Riedl. 2003 · 2003
Earlier work this paper cites.
How many turkers are there.(dec 2009)
Greg Little. 2009 · 2009
Earlier work this paper cites.
Tell me more?: the effects of mental model soundness on personalizing an intelligent agent
Todd Kulesza, Simone Stumpf, Margaret Burnett, and Irwin Kwan. 2012 · 2012
Earlier work this paper cites.
Legibility and predictability of robot motion
Anca D Dragan, Kenton CT Lee, and Siddhartha S Srinivasa. 2013 · 2013
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Towards transparent systems: Semantic characterization of failure modes
Aayush Bansal, Ali Farhadi, and Devi Parikh. 2014 · 2014
Earlier work this paper cites.
A multi-world approach to question answering about real-world scenes based on uncertain input
Mateusz Malinowski and Mario Fritz. 2014 · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus. 2014 · 2014
Cited alongside, same era.
Predicting failures of vision systems
Peng Zhang, Jiuling Wang, Ali Farhadi, Martial Hebert, and Devi Parikh. 2014 · 2014
Cited alongside, same era.
Vqa: Visual question answering
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. 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.
Analyzing the behavior of visual question answering models
Aishwarya Agrawal, Dhruv Batra, and Devi Parikh. 2016 · 2016
Cited alongside, same era.
Generating visual explanations
Why that nao?: How humans adapt to a conventional humanoid robot in taking turns-at-talk
Hannah RM Pelikan and Mathias Broth. 2016 · 2016
Later among the works it cites.
Question relevance in vqa: Identifying non-visual and false-premise questions
Arijit Ray, Gordon Christie, Mohit Bansal, Dhruv Batra, and Devi Parikh. 2016 · 2016
Later among the works it cites.
Why should i trust you?: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Later among the works it cites.
Learning language games through interaction
Sida I Wang, Percy Liang, and Christopher D Manning. 2016 · 2016
Later among the works it cites.
Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. 2016 · 2016
Later among the works it cites.
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Lisa Anne Hendricks, Zeynep Akata, Marcus Rohrbach, Jeff Donahue, Bernt Schiele, and Trevor Darrell. 2016 · 2016
Cited alongside, same era.
Hierarchical question-image co-attention for visual question answering
Jiasen Lu, Jianwei Yang, Dhruv Batra, and Devi Parikh. 2016 · 2016
Cited alongside, same era.
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. 2016a
Cited in the paper.
Towards transparent ai systems: Interpreting visual question answering models
Yash Goyal, Akrit Mohapatra, Devi Parikh, and Dhruv Batra. 2016b
Cited in the paper.
Grad-cam: Why did you say that? visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, and Dhruv Batra. 2017 · 2017
Later among the works it cites.
Menaka Narayanan, Emily Chen, Jeffrey He, Been Kim, Sam Gershman, and Finale Doshi-Velez. 2018 · 2018
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