Fetching the paper…
Reading the bibliography…
Despite recent advances in Visual QuestionAnswering (VQA), it remains a challenge todetermine how much success can be attributedto sound reasoning and comprehension ability.We seek to investigate this question by propos-ing a new task ofrationale generation.
On information and sufficiency
S. Kullback and R. A. Leibler. 1951 · 1951
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.
ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
How do you tell a blackbird from a crow?
Thomas Berg and Peter N. Belhumeur. 2013 · 2013
Earlier work this paper cites.
Exploring models and data for image question answering
Mengye Ren, Ryan Kiros, and Richard Zemel. 2015 · 2015
Earlier work this paper cites.
Analyzing the behavior of visual question answering models
Aishwarya Agrawal, Dhruv Batra, and Devi Parikh. 2016a · 2016
Earlier work this paper cites.
Neural module networks
Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Dan Klein. 2016 · 2016
Earlier work this paper cites.
Towards transparent ai systems: Interpreting visual question answering models
Yash Goyal, Akrit Mohapatra, Devi Parikh, and Dhruv Batra. 2016 · 2016
Earlier work this paper cites.
Generating visual explanations
Lisa Anne Hendricks, Zeynep Akata, Marcus Rohrbach, Jeff Donahue, Bernt Schiele, and Trevor Darrell. 2016 · 2016
Earlier work this paper cites.
Revisiting visual question answering baselines
Allan Jabri, Armand Joulin, and Laurens van der Maaten. 2016 · 2016
Earlier work this paper cites.
Supervised learning of universal sentence representations from natural language inference data
Alexis Conneau, Douwe Kiela, Holger Schwenk, Loïc Barrault, and Antoine Bordes. 2017 · 2017
Earlier work this paper cites.
Human attention in visual question answering: Do humans and deep networks look at the same regions?
Abhishek Das, Harsh Agrawal, Larry Zitnick, Devi Parikh, and Dhruv Batra. 2017 · 2017
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. 2017 · 2017
Cited alongside, same era.
Learning to reason: End-to-end module networks for visual question answering
Ronghang Hu, Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Kate Saenko. 2017 · 2017
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
Cited alongside, same era.
Don’t just assume; look and answer: Overcoming priors for visual question answering
Aishwarya Agrawal, Dhruv Batra, Devi Parikh, and Aniruddha Kembhavi. 2018 · 2018
Cited alongside, same era.
Transparency by design: Closing the gap between performance and interpretability in visual reasoning
David Mascharka, Philip Tran, Ryan Soklaski, and Arjun Majumdar. 2018 · 2018
Later among the works it cites.
Overcoming language priors in visual question answering with adversarial regularization
Sainandan Ramakrishnan, Aishwarya Agrawal, and Stefan Lee. 2018 · 2018
Later among the works it cites.
From recognition to cognition: Visual commonsense reasoning
Rowan Zellers, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2018 · 2018
Later among the works it cites.
Rubi: Reducing unimodal biases in visual question answering
Remi Cadene, Corentin Dancette, Hedi Ben-younes, Matthieu Cord, and Devi Parikh. 2019 · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Do explanations make vqa models more predictable to a human?
Arjun Chandrasekaran, Viraj Prabhu, Deshraj Yadav, Prithvijit Chattopadhyay, and Devi Parikh. 2018 · 2018
Cited alongside, same era.
Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
Roberto Cipolla, Yarin Gal, and Alex Kendall. 2018 · 2018
Cited alongside, same era.
Content-based image retrieval using generated textual meta-data
Kexin Huang. 2018 · 2018
Cited alongside, same era.
Pythia v0.1: the winning entry to the vqa challenge 2018
Yu Jiang, Vivek Natarajan, Xinlei Chen, Marcus Rohrbach, Dhruv Batra, and Devi Parikh. 2018 · 2018
Cited alongside, same era.
Explicit bias discovery in visual question answering models
Varun Manjunatha, Nirat Saini, and Larry S. Davis. 2018 · 2018
Cited alongside, same era.
Vqa: Visual question answering
Aishwarya Agrawal, Jiasen Lu, Stanislaw Antol, Margaret Mitchell, C. Lawrence Zitnick, Devi Parikh, and Dhruv Batra. 2016b
Cited in the paper.
Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. 2019 · 2019
Later among the works it cites.
Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks
Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. 2019 · 2019
Later among the works it cites.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Later among the works it cites.
Explain yourself! leveraging language models for commonsense reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 2019
Later among the works it cites.
Cycle-consistency for robust visual question answering
Meet Shah, Xinlei Chen, Marcus Rohrbach, and Devi Parikh. 2019 · 2019
Later among the works it cites.
Probabilistic neural-symbolic models for interpretable visual question answering
Ramakrishna Vedantam, Karan Desai, Stefan Lee, Marcus Rohrbach, Dhruv Batra, and Devi Parikh. 2019 · 2019
Later among the works it cites.