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Visual Question Answering (VQA) is a challenging task of natural language processing (NLP) and computer vision (CV), attracting significant attention from researchers.
Visual question answering dataset for bilingual image understanding: A study of cross-lingual transfer using attention maps
Nobuyuki Shimizu, Na Rong, and Takashi Miyazaki. 2018 · 1928
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.
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, et al. 2020 · 2010
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.
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 · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. 2015 · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
Earlier work this paper cites.
Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
Earlier work this paper cites.
Visual7w: Grounded question answering in images
Yuke Zhu, Oliver Groth, Michael Bernstein, and Li Fei-Fei. 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.
Survey of visual question answering: Datasets and techniques
Akshay Kumar Gupta. 2017 · 2017
Earlier work this paper cites.
Tips and tricks for visual question answering: Learnings from the 2017 challenge
Damien Teney, Peter Anderson, Xiaodong He, and Anton Van Den Hengel. 2018 · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Cascade r-cnn: Delving into high quality object detection
Zhaowei Cai and Nuno Vasconcelos. 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
Cited alongside, same era.
VnCoreNLP: A Vietnamese natural language processing toolkit
Thanh Vu, Dat Quoc Nguyen, Dai Quoc Nguyen, Mark Dras, and Mark Johnson. 2018 · 2018
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
In defense of grid features for visual question answering
Huaizu Jiang, Ishan Misra, Marcus Rohrbach, Erik Learned-Miller, and Xinlei Chen. 2020 · 2020
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Beit: Bert pre-training of image transformers
Hangbo Bao, Li Dong, and Furu Wei. 2021 · 2021
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Humair Raj Khan, Deepak Gupta, and Asif Ekbal. 2021 · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. 2021 · 2021
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xgqa: Cross-lingual visual question answering
Jonas Pfeiffer, Gregor Geigle, Aishwarya Kamath, Jan-Martin O Steitz, Stefan Roth, Ivan Vulić, and Iryna Gurevych. 2021 · 2021
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Jacob 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.
Ok-vqa: A visual question answering benchmark requiring external knowledge
Kenneth Marino, Mohammad Rastegari, Ali Farhadi, and Roozbeh Mottaghi. 2019 · 2019
Cited alongside, same era.
Towards vqa models that can read
Amanpreet Singh, Vivek Natarajan, Meet Shah, Yu Jiang, Xinlei Chen, Dhruv Batra, Devi Parikh, and Marcus Rohrbach. 2019 · 2019
Cited alongside, same era.
A unified framework for multilingual and code-mixed visual question answering
Deepak Gupta, Pabitra Lenka, Asif Ekbal, and Pushpak Bhattacharyya. 2020 · 2020
Cited alongside, same era.
All you may need for vqa are image captions
Soravit Changpinyo, Doron Kukliansky, Idan Szpektor, Xi Chen, Nan Ding, and Radu Soricut. 2022a
Cited in the paper.
Towards multi-lingual visual question answering
Soravit Changpinyo, Linting Xue, Idan Szpektor, Ashish V Thapliyal, Julien Amelot, Xi Chen, and Radu Soricut. 2022b
Cited in the paper.
Later among the works it cites.
Human-adversarial visual question answering
Sasha Sheng, Amanpreet Singh, Vedanuj Goswami, Jose Magana, Tristan Thrush, Wojciech Galuba, Devi Parikh, and Douwe Kiela. 2021 · 2021
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Delving deeper into cross-lingual visual question answering
Chen Liu, Jonas Pfeiffer, Anna Korhonen, Ivan Vulic, and Iryna Gurevych. 2022 · 2022
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Improving the cross-lingual generalisation in visual question answering
Farhad Nooralahzadeh and Rico Sennrich. 2022 · 2022
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Dureadervis: A: A chinese dataset for open-domain document visual question answering
Le Qi, Shangwen Lv, Hongyu Li, Jing Liu, Yu Zhang, Qiaoqiao She, Hua Wu, Haifeng Wang, and Ting Liu. 2022 · 2022
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A-okvqa: A benchmark for visual question answering using world knowledge
Dustin Schwenk, Apoorv Khandelwal, Christopher Clark, Kenneth Marino, and Roozbeh Mottaghi. 2022 · 2022
Later among the works it cites.