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The Visual Question Answering (VQA) task aspires to provide a meaningful testbed for the development of AI models that can jointly reason over visual and natural language inputs.
ConceptNet—a practical commonsense reasoning tool-kit
Hugo Liu and Push Singh · 2004
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METEOR: An automatic metric for MT evaluation with improved correlation with human judgments
Satanjeev Banerjee and Alon Lavie · 2005
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Learning structured embeddings of knowledge bases
Antoine Bordes, Jason Weston, Ronan Collobert, and Yoshua Bengio · 2011
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Semantic parsing on freebase from question-answer pairs
Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang · 2013
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Question answering with subgraph embeddings
Antoine Bordes, Sumit Chopra, and Jason Weston · 2014
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Microsoft COCO: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 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
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GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning · 2014
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Information extraction over structured data: Question answering with Freebase
Xuchen Yao and Benjamin Van Durme · 2014
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Capturing long-tail distributions of object subcategories
Xiangxin Zhu, Dragomir Anguelov, and Deva Ramanan · 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
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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
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Are you talking to a machine? dataset and methods for multilingual image question
Haoyuan Gao, Junhua Mao, Jie Zhou, Zhiheng Huang, Lei Wang, and Wei Xu · 2015
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Visual turing test for computer vision systems
Donald Geman, Stuart Geman, Neil Hallonquist, and Laurent Younes · 2015
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Exploring models and data for image question answering
Mengye Ren, Jamie Kiros, and Richard S. Zemel · 2015
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WikiQA: A challenge dataset for open-domain question answering
Yi Yang, Wen-tau Yih, and Christopher Meek · 2015
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Visual madlibs: Fill in the blank description generation and question answering
Licheng Yu, Eunbyung Park, Alexander C. Berg, and Tamara L. Berg · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
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MovieQA: understanding stories in movies through question-answering
Makarand Tapaswi, Yukun Zhu, Rainer Stiefelhagen, Antonio Torralba, Raquel Urtasun, and Sanja Fidler · 2016
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Visual7W: grounded question answering in images
Yuke Zhu, Oliver Groth, Michael S. Bernstein, and Li Fei-Fei · 2016
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Reading Wikipedia to answer open-domain questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes · 2017
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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
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Automatic understanding of image and video advertisements
Zaeem Hussain, Mingda Zhang, Xiaozhong Zhang, Keren Ye, C. Thomas, Zuha Agha, Nathan Ong, and Adriana Kovashka · 2017
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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
Cited alongside, same era.
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 S. Bernstein, and Li Fei-Fei · 2017
Cited alongside, same era.
Explicit knowledge-based reasoning for visual question answering
Peng Wang, Qi Wu, Chunhua Shen, Anthony R. Dick, and Anton van den Hengel · 2017
Cited alongside, same era.
KVQA: Knowledge-aware visual question answering
Sanket Shah, Anand Mishra, Naganand Yadati, and Partha Pratim Talukdar · 2019
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Towards VQA models that can read
Amanpreet Singh, Vivek Natarajan, Meet Shah, Yu Jiang, Xinlei Chen, Dhruv Batra, Devi Parikh, and Marcus Rohrbach · 2019
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CommonsenseQA: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant · 2019
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LXMERT: Learning cross-modality encoder representations from transformers
Hao Hao Tan and Mohit Bansal · 2019
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End-to-end open-domain question answering with BERTserini
Wei Yang, Yuqing Xie, Aileen Lin, Xingyu Li, Luchen Tan, Kun Xiong, Ming Li, and Jimmy Lin · 2019
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From recognition to cognition: Visual commonsense reasoning
Rowan Zellers, Yonatan Bisk, Ali Farhadi, and Yejin Choi · 2019
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FVQA: fact-based visual question answering
Peng Wang, Qi Wu, Chunhua Shen, Anton van den Hengel, and Anthony R. Dick · 2017
Cited alongside, same era.
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
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
Cited alongside, same era.
Tell-and-answer: Towards explainable visual question answering using attributes and captions
Qing Li, Jianlong Fu, Dongfei Yu, Tao Mei, and Jiebo Luo · 2018
Cited alongside, same era.
Multimodal explanations: Justifying decisions and pointing to the evidence
Dong Huk Park, Lisa Anne Hendricks, Zeynep Akata, Anna Rohrbach, Bernt Schiele, Trevor Darrell, and Marcus Rohrbach · 2018
Cited alongside, same era.
A call for clarity in reporting BLEU scores
Matt Post · 2018
Cited alongside, same era.
Know what you don’t know: Unanswerable questions for SQuAD
Pranav Rajpurkar, Robin Jia, and Percy Liang · 2018
Cited alongside, same era.
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, T. J. Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeff Wu, Clemens Winter, Christopher 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
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Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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KnowIT VQA: Answering knowledge-based questions about videos
Noa García, Mayu Otani, Chenhui Chu, and Yuta Nakashima · 2020
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12-in-1: Multi-task vision and language representation learning
Jiasen Lu, Vedanuj Goswami, Marcus Rohrbach, Devi Parikh, and Stefan Lee · 2020
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VisualCOMET: Reasoning about the dynamic context of a still image
Jae Sung Park, Chandra Bhagavatula, Roozbeh Mottaghi, Ali Farhadi, and Yejin Choi · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam M. Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2020
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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
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WebQA: Multihop and multimodal qa
Yingshan Chang, Mridu Baldevraj Narang, Hisami Suzuki, Guihong Cao, Jianfeng Gao, and Yonatan Bisk · 2021
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Select, substitute, search: A new benchmark for knowledge-augmented visual question answering
Aman Jain, Mayank Kothyari, Vishwajeet Kumar, Preethi Jyothi, Ganesh Ramakrishnan, and Soumen Chakrabarti · 2021
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KRISP: Integrating implicit and symbolic knowledge for open-domain knowledge-based VQA
Kenneth Marino, Xinlei Chen, Devi Parikh, Abhinav Kumar Gupta, and Marcus Rohrbach · 2021
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ClipCap: CLIP prefix for image captioning
Ron Mokady, Amir Hertz, and Amit H. Bermano · 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
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Symbolic knowledge distillation: from general language models to commonsense models
Peter West, Chandra Bhagavatula, Jack Hessel, Jena D Hwang, Liwei Jiang, Ronan Le Bras, Ximing Lu, Sean Welleck, and Yejin Choi · 2021
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An empirical study of GPT-3 for few-shot knowledge-based VQA
Zhengyuan Yang, Zhe Gan, Jianfeng Wang, Xiaowei Hu, Yumao Lu, Zicheng Liu, and Lijuan Wang · 2021
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VinVL: Revisiting visual representations in vision-language models
Pengchuan Zhang, Xiujun Li, Xiaowei Hu, Jianwei Yang, Lei Zhang, Lijuan Wang, Yejin Choi, and Jianfeng Gao · 2021
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Webly supervised concept expansion for general purpose vision models
Amita Kamath, Christopher Clark, Tanmay Gupta, Eric Kolve, Derek Hoiem, and Aniruddha Kembhavi · 2022
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