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Vision-and-language (V&L) models pretrained on large-scale multimodal data have demonstrated strong performance on various tasks such as image captioning and visual question answering (VQA).
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 1901
Earlier work this paper cites.
Visual entailment: A novel task for fine-grained image understanding
Ning Xie, Farley Lai, Derek Doran, and Asim Kadav. 2019 · 1901
Earlier work this paper cites.
Unifying vision-and-language tasks via text generation
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Earlier work this paper cites.
Wordnet: A lexical database for english
George A. Miller. 1995 · 1995
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009 · 2009
Earlier work this paper cites.
Im2text: Describing images using 1 million captioned photographs
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Unbiased look at dataset bias
Antonio Torralba and Alexei A. Efros. 2011 · 2011
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Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. 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.
Privacy concerns and behaviors of people with visual impairments
Tousif Ahmed, Roberto Hoyle, Kay Connelly, David Crandall, and Apu Kapadia. 2015 · 2015
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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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Faster R-CNN: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2015 · 2015
Earlier work this paper cites.
Stacked attention networks for image question answering
Zichao Yang, Xiaodong He, Jianfeng Gao, Li Deng, and Alexander J. Smola. 2015 · 2015
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Revisiting visual question answering baselines
Allan Jabri, Armand Joulin, and Laurens van der Maaten. 2016 · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. 2016 · 2016
Earlier work this paper cites.
C-VQA: A compositional split of the visual question answering (vqa) v1.0 dataset
Aishwarya Agrawal, Aniruddha Kembhavi, Dhruv Batra, and Devi Parikh. 2017 · 2017
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, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C. Lawrence Zitnick, and Ross Girshick. 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, et al. 2017 · 2017
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollar, Zhuowen Tu, and Kaiming He. 2017 · 2017
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Deep learning using rectified linear units (relu)
Abien Fred Agarap. 2018 · 2018
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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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CrossVQA: Scalably generating benchmarks for systematically testing VQA generalization
Arjun Akula, Soravit Changpinyo, Boqing Gong, Piyush Sharma, Song-Chun Zhu, and Radu Soricut. 2021 · 2021
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Multimodal pretraining unmasked: A meta-analysis and a unified framework of vision-and-language BERTs
Emanuele Bugliarello, Ryan Cotterell, Naoaki Okazaki, and Desmond Elliott. 2021 · 2021
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Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts
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Beyond question-based biases: Assessing multimodal shortcut learning in visual question answering
Corentin Dancette, Rémi Cadène, Damien Teney, and Matthieu Cord. 2021 · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2021 · 2021
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Bottom-up and top-down attention for image captioning and visual question answering
Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, and Lei Zhang. 2018 · 2018
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Cross-dataset adaptation for visual question answering
Wei-Lun Chao, Hexiang Hu, and Fei Sha. 2018 · 2018
Cited alongside, same era.
VizWiz grand challenge: Answering visual questions from blind people
Danna Gurari, Qing Li, Abigale J. Stangl, Anhong Guo, Chi Lin, Kristen Grauman, Jiebo Luo, and Jeffrey P. Bigham. 2018 · 2018
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Conceptual Captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning
Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. 2018 · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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GQA: A new dataset for real-world visual reasoning and compositional question answering
Drew A. Hudson and Christopher D. Manning. 2019 · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2019 · 2019
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Counterfactual VQA: A cause-effect look at language bias
Yulei Niu, Kaihua Tang, Hanwang Zhang, Zhiwu Lu, Xian-Sheng Hua, and Ji-Rong Wen. 2021 · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021 · 2021
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LAION-400M: Open dataset of clip-filtered 400 million image-text pairs
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Human-adversarial visual question answering
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Training data-efficient image transformers & distillation through attention
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Multimodal few-shot learning with frozen language models
Maria Tsimpoukelli, Jacob L. Menick, Serkan Cabi, S. M. Ali Eslami, Oriol Vinyals, and Felix Hill. 2021 · 2021
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Domain-robust VQA with diverse datasets and methods but no target labels
Mingda Zhang, Tristan D. Maidment, Ahmad Diab, Adriana Kovashka, and Rebecca Hwa. 2021 · 2021
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Flamingo: a visual language model for few-shot learning
Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, Roman Ring, Eliza Rutherford, Serkan Cabi, Tengda Han, Zhitao Gong, Sina Samangooei, Marianne Monteiro, Jacob Menick, Sebastian Borgeaud, Andrew Brock, Aida Nematzadeh, Sahand Sharifzadeh, Mikolaj Binkowski, Ricardo Barreira, Oriol Vinyals, Andrew Zisserman, and Karen Simonyan. 2022 · 2022
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CARETS: A consistency and robustness evaluative test suite for VQA
Carlos E. Jimenez, Olga Russakovsky, and Karthik Narasimhan. 2022 · 2022
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AJ Piergiovanni, Wei Li, Weicheng Kuo, Mohammad Saffar, Fred Bertsch, and Anelia Angelova. 2022 · 2022
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CLIP models are few-shot learners: Empirical studies on VQA and visual entailment
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