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Multimodal image-language transformers have achieved impressive results on a variety of tasks that rely on fine-tuning (e.g., visual question answering and image retrieval).
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 · 1908
Earlier work this paper cites.
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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Are we pretraining it right? digging deeper into visio-linguistic pretraining
Amanpreet Singh, Vedanuj Goswami, and Devi Parikh. 2020 · 2004
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Arjun R Akula, Spandana Gella, Yaser Al-Onaizan, Song-Chun Zhu, and Siva Reddy. 2020 · 2005
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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 · 2005
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2013
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Microsoft coco: Common objects in context
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.
From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions
Peter Young, Alice Lai, Micah Hodosh, and Julia Hockenmaier. 2014 · 2014
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HICO: A benchmark for recognizing human-object interactions in images
Yu-Wei Chao, Zhan Wang, Yugeng He, Jiaxuan Wang, and Jia Deng. 2015 · 2015
Earlier work this paper cites.
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
Cited alongside, same era.
Saurabh Gupta and Jitendra Malik. 2015 · 2015
Cited alongside, same era.
Situation recognition: Visual semantic role labeling for image understanding
Mark Yatskar, Luke Zettlemoyer, and Ali Farhadi. 2016 · 2016
Cited alongside, same era.
Vse++: Improving visual-semantic embeddings with hard negatives
Fartash Faghri, David J Fleet, Jamie Ryan Kiros, and Sanja Fidler. 2017 · 2017
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
Defoiling foiled image captions
Pranava Madhyastha, Josiah Wang, and Lucia Specia. 2018 · 2018
Later among the works it cites.
Object hallucination in image captioning
Anna Rohrbach, Lisa Anne Hendricks, Kaylee Burns, Trevor Darrell, and Kate Saenko. 2018 · 2018
Later among the works it cites.
Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning
Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. 2018 · 2018
Later among the works it cites.
Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks
Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. 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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UNITER: Universal image-text representation learning
Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. 2020 · 2020
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Cited alongside, same era.
FOIL it! find one mismatch between image and language caption
Ravi Shekhar, Sandro Pezzelle, Yauhen Klimovich, Aurélie Herbelot, Moin Nabi, Enver Sangineto, and Raffaella Bernardi. 2017 · 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.
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.
Grounding visual explanations
Lisa Anne Hendricks, Ronghang Hu, Trevor Darrell, and Zeynep Akata. 2018 · 2018
Cited alongside, same era.
Unicoder-vl: A universal encoder for vision and language by cross-modal pre-training
Gen Li, Nan Duan, Yuejian Fang, Ming Gong, and Daxin Jiang. 2020a
Cited in the paper.
Oscar: Object-semantics aligned pre-training for vision-language tasks
Xiujun Li, Xi Yin, Chunyuan Li, Xiaowei Hu, Pengchuan Zhang, Lei Zhang, Lijuan Wang, Houdong Hu, Li Dong, Furu Wei, et al. 2020b
Cited in the paper.
Visual relationship detection with language priors
Cewu Lu, Ranjay Krishna, Michael Bernstein, and Li Fei-Fei. 2016a
Cited in the paper.
Later among the works it cites.
Contrastive learning for weakly supervised phrase grounding
Tanmay Gupta, Arash Vahdat, Gal Chechik, Xiaodong Yang, Jan Kautz, and Derek Hoiem. 2020 · 2020
Later among the works it cites.
Decoupling the role of data, attention, and losses in multimodal transformers
Lisa Anne Hendricks, John Mellor, Rosalia Schneider, Jean-Baptiste Alayrac, and Aida Nematzadeh. 2021 · 2021
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