Fetching the paper…
Reading the bibliography…
We introduce CARETS, a systematic test suite to measure consistency and robustness of modern VQA models through a series of six fine-grained capability tests.
Wordnet: A lexical database for english
George A. Miller. 1995 · 1995
Earlier work this paper cites.
80 million tiny images: A large data set for nonparametric object and scene recognition
A. Torralba, R. Fergus, and W. Freeman. 2008 · 2008
Earlier work this paper cites.
Microsoft coco: Common objects in context
Tsung-Yi Lin, M. Maire, Serge J. Belongie, James Hays, P. Perona, D. Ramanan, Piotr Dollár, and C. L. Zitnick. 2014 · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
Earlier work this paper cites.
VQA: Visual Question Answering
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, and Devi Parikh. 2015 · 2015
Earlier work this paper cites.
Image retrieval using scene graphs
Justin Johnson, Ranjay Krishna, Michael Stark, Li Jia Li, David A. Shamma, Michael S. Bernstein, and Fei Fei Li. 2015 · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross B. Girshick, and J. Sun. 2015 · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
Earlier work this paper cites.
Yin and yang: Balancing and answering binary visual questions
P. Zhang, Yash Goyal, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh. 2016 · 2016
Earlier work this paper cites.
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
Earlier work this paper cites.
CLEVR: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Li Fei-Fei, Bharath Hariharan, C. Lawrence Zitnick, Laurens Van Der Maaten, and Ross Girshick. 2017 · 2017
Earlier work this paper cites.
An analysis of visual question answering algorithms
Kushal Kafle and Christopher Kanan. 2017 · 2017
Earlier work this paper cites.
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, and et al. 2017 · 2017
Cited alongside, same era.
Ashish Vaswani, Noam M. Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Aggregated residual transformations for deep neural networks
Saining Xie, Ross B. Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He. 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.
Bilinear attention networks
Jin-Hwa Kim, Jaehyun Jun, and Byoung-Tak Zhang. 2018 · 2018
Cited alongside, same era.
Are red roses red? Evaluating consistency of question-answering models
Marco Tulio Ribeiro, Carlos Guestrin, and Sameer Singh. 2020a · 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.
Roses are red, violets are blue… but should vqa expect them to?
Corentin Kervadec, Grigory Antipov, Moez Baccouche, and Christian Wolf. 2020 · 2020
Later among the works it cites.
Squinting at vqa models: Introspecting vqa models with sub-questions
Ramprasaath R. Selvaraju, Purva Tendulkar, Devi Parikh, E. Horvitz, Marco Túlio Ribeiro, Besmira Nushi, and Ece Kamar. 2020 · 2020
Later among the works it cites.
Mmf: A multimodal framework for vision and language research
Amanpreet Singh, Vedanuj Goswami, Vivek Natarajan, Yu Jiang, Xinlei Chen, Meet Shah, Marcus Rohrbach, Dhruv Batra, and Devi Parikh. 2020 · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Vqa-e: Explaining, elaborating, and enhancing your answers for visual questions
Qing Li, Qingyi Tao, Shafiq Joty, Jianfei Cai, and Jiebo Luo. 2018 · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
GQA: A new dataset for real-world visual reasoning and compositional question answering
Drew A. Hudson and Christopher D. Manning. 2019 · 2019
Cited alongside, same era.
Supervised multimodal bitransformers for classifying images and text
Douwe Kiela, Suvrat Bhooshan, Hamed Firooz, Ethan Perez, and Davide Testuggine. 2019 · 2019
Cited alongside, same era.
Visualbert: A simple and performant baseline for vision and language
Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, and Kai-Wei Chang. 2019 · 2019
Cited alongside, same era.
Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks
Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. 2019 · 2019
Cited alongside, same era.
Explicit bias discovery in visual question answering models
Varun Manjunatha, Nirat Saini, and Larry S. Davis. 2019 · 2019
Cited alongside, same era.
LXMert: Learning cross-modality encoder representations from transformers
Hao Tan and Mohit Bansal. 2020 · 2020
Later among the works it cites.
BLiMP: The Benchmark of Linguistic Minimal Pairs for English
Alex Warstadt, Alicia Parrish, Haokun Liu, Anhad Mohananey, Wei Peng, Sheng-Fu Wang, and Samuel R. Bowman. 2020 · 2020
Later among the works it cites.
Yonatan Bitton, Gabriel Stanovsky, Roy Schwartz, and Michael Elhadad. 2021 · 2021
Later among the works it cites.
Generate your counterfactuals: Towards controlled counterfactual generation for text
Nishtha Madaan, Inkit Padhi, Naveen Panwar, and Diptikalyan Saha. 2021 · 2021
Later among the works it cites.
Generating datasets with pretrained language models
Timo Schick and Hinrich Schütze. 2021 · 2021
Later among the works it cites.
Polyjuice: Generating counterfactuals for explaining, evaluating, and improving models
Tongshuang Wu, Marco Tulio Ribeiro, Jeffrey Heer, and Daniel Weld. 2021 · 2021
Later among the works it cites.
A study of face obfuscation in imagenet
Kaiyu Yang, Jacqueline Yau, Li Fei-Fei, Jia Deng, and Olga Russakovsky. 2021 · 2021
Later among the works it cites.