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Pre-trained contextual vision-and-language (V&L) models have achieved impressive performance on various benchmarks.
ViLBERT: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks
Jiasen Lu, Batra Dhruv, Parikh Devi, and Lee Lee. 2019 · 1908
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VL-BERT: Pre-training of generic visual-linguistic representations
Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, and Jifeng Dai. 2019 · 1908
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020b · 2002
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Pixel-bert: Aligning image pixels with text by deep multi-modal transformers
Zhicheng Huang, Zhaoyang Zeng, Bei Liu, Dongmei Fu, and Jianlong Fu. 2020 · 2004
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Probing text models for common ground with visual representations
Gabriel Ilharco, Rowan Zellers, Ali Farhadi, and Hannaneh Hajishirzi. 2020 · 2005
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The hateful memes challenge: Detecting hate speech in multimodal memes
Douwe Kiela, Hamed Firooz, Aravind Mohan, Vedanuj Goswami, Amanpreet Singh, Pratik Ringshia, and Davide Testuggine. 2020 · 2005
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Large-scale adversarial training for vision-and-language representation learning
Zhe Gan, Yen-Chun Chen, Linjie Li, Chen Zhu, Yu Cheng, and Jingjing Liu. 2020 · 2006
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Ernie-vil: Knowledge enhanced vision-language representations through scene graph
Fei Yu, Jiji Tang, Weichong Yin, Yu Sun, Hao Tian, Hua Wu, and Haifeng Wang. 2020 · 2006
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Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton. 2008 · 2008
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Vivo: Surpassing human performance in novel object captioning with visual vocabulary pre-training
Xiaowei Hu, Xi Yin, Kevin Lin, Lijuan Wang, Lei Zhang, Jianfeng Gao, and Zicheng Liu. 2020 · 2009
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Im2text: Describing images using 1 million captioned photographs
Vicente Ordonez, Girish Kulkarni, and Tamara L. Berg. 2011 · 2011
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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 · 2015
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Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A Efros. 2015 · 2015
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Deep visual-semantic alignments for generating image descriptions
Andrej Karpathy and Li Fei-Fei. 2015 · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2015 · 2015
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Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models
Bryan A Plummer, Liwei Wang, Chris M Cervantes, Juan C Caicedo, Julia Hockenmaier, and Svetlana Lazebnik. 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
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Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro. 2016 · 2016
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Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros. 2016 · 2016
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Grounding of textual phrases in images by reconstruction
Anna Rohrbach, Marcus Rohrbach, Ronghang Hu, Trevor Darrell, and Bernt Schiele. 2016 · 2016
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Modeling context in referring expressions
Licheng Yu, Patrick Poirson, Shan Yang, Alexander C Berg, and Tamara L Berg. 2016 · 2016
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.
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
Cited alongside, same era.
A survey on hate speech detection using natural language processing
Anna Schmidt and Michael Wiegand. 2017 · 2017
Cited alongside, same era.
Revisiting unreasonable effectiveness of data in deep learning era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta. 2017 · 2017
Cited alongside, same era.
Revisiting self-supervised visual representation learning
Alexander Kolesnikov, Xiaohua Zhai, and Lucas Beyer. 2019 · 2019
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Towards unsupervised image captioning with shared multimodal embeddings
Iro Laina, Christian Rupprecht, and Nassir Navab. 2019 · 2019
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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
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Linguistic knowledge and transferability of contextual representations
Nelson F. Liu, Matt Gardner, Yonatan Belinkov, Matthew E. Peters, and Noah A. Smith. 2019 · 2019
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How multilingual is multilingual BERT?
Telmo Pires, Eva Schlinger, and Dan Garrette. 2019 · 2019
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A corpus for reasoning about natural language grounded in photographs
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Weakly-supervised visual grounding of phrases with linguistic structures
Fanyi Xiao, Leonid Sigal, and Yong Jae Lee. 2017 · 2017
Cited alongside, same era.
Men also like shopping: Reducing gender bias amplification using corpus-level constraints
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2017 · 2017
Cited alongside, same era.
Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015 · 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 · 2018
Cited alongside, same era.
Unsupervised machine translation using monolingual corpora only
Guillaume Lample, Alexis Conneau, Ludovic Denoyer, and Marc’Aurelio Ranzato. 2018 · 2018
Cited alongside, same era.
Stacked cross attention for image-text matching
Kuang-Huei Lee, Xi Chen, Gang Hua, Houdong Hu, and Xiaodong He. 2018 · 2018
Cited alongside, same era.
Alane Suhr, Stephanie Zhou, Iris Zhang, Huajun Bai, and Yoav Artzi. 2019 · 2019
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LXMERT: Learning cross-modality encoder representations from transformers
Hao Tan and Mohit Bansal. 2019 · 2019
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Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT
Shijie Wu and Mark Dredze. 2019 · 2019
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Gender bias in contextualized word embeddings
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Ryan Cotterell, Vicente Ordonez, and Kai-Wei Chang. 2019 · 2019
Later among the works it cites.
Behind the scene: Revealing the secrets of pre-trained vision-and-language models
Jize Cao, Zhe Gan, Yu Cheng, Licheng Yu, Yen-Chun Chen, and Jingjing Liu. 2020 · 2020
Closest in time.
Emerging cross-lingual structure in pretrained language models
Alexis Conneau, Shijie Wu, Haoran Li, Luke Zettlemoyer, and Veselin Stoyanov. 2020 · 2020
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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. 2020 · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. 2020 · 2020
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The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale
Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali, Stefan Popov, Matteo Malloci, Alexander Kolesnikov, Tom Duerig, and Vittorio Ferrari. 2020 · 2020
Closest in time.
A comparison of pre-trained vision-and-language models for multimodal representation learning across medical images and reports
Yikuan Li, Hanyin Wang, and Yuan Luo. 2020c · 2020
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Vokenization: Improving language understanding with contextualized, visual-grounded supervision
Hao Tan and Mohit Bansal. 2020 · 2020
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MAF: Multimodal alignment framework for weakly-supervised phrase grounding
Qinxin Wang, Hao Tan, Sheng Shen, Michael Mahoney, and Zhewei Yao. 2020 · 2020
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Unified vision-language pre-training for image captioning and vqa
Luowei Zhou, Hamid Palangi, Lei Zhang, Houdong Hu, Jason J Corso, and Jianfeng Gao. 2020 · 2020
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Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts
Soravit Changpinyo, Piyush Sharma, Nan Ding, and Radu Soricut. 2021 · 2021
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