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Diffusion models, such as Stable Diffusion, have shown incredible performance on text-to-image generation.
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.
On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes
Andrew Ng and Michael Jordan. 2001 · 2001
Earlier work this paper cites.
Exploiting cloze questions for few shot text classification and natural language inference
Timo Schick and Hinrich Schütze. 2020 · 2001
Earlier work this paper cites.
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 · 2005
Earlier work this paper cites.
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon. 2020 · 2010
Earlier work this paper cites.
On deep generative models with applications to recognition
Marc’Aurelio Ranzato, Joshua Susskind, Volodymyr Mnih, and Geoffrey Hinton. 2011 · 2011
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling. 2013 · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
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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.
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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Deep visual-semantic alignments for generating image descriptions
Andrej Karpathy and Li Fei-Fei. 2015 · 2015
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Modeling context in referring expressions
Licheng Yu, Patrick Poirson, Shan Yang, Alexander C Berg, and Tamara L Berg. 2016 · 2016
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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 · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. 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
Earlier work this paper cites.
Neural discrete representation learning
Aaron Van DenOord, Oriol Vinyals, and etal. 2017 · 2017
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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
Earlier work this paper cites.
Shane Barratt and Rishi Sharma. 2018 · 2018
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Stacked cross attention for image-text matching
Kuang-Huei Lee, Xi Chen, Gang Hua, Houdong Hu, and Xiaodong He. 2018 · 2018
Earlier work this paper cites.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon. 2019 · 2019
Earlier work this paper cites.
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
Earlier work this paper cites.
Label-efficient semantic segmentation with diffusion models
Dmitry Baranchuk, Ivan Rubachev, Andrey Voynov, Valentin Khrulkov, and Artem Babenko. 2021 · 2021
Cited alongside, same era.
Accurately computing the log-sum-exp and softmax functions
Pierre Blanchard, Desmond J Higham, and Nicholas J Higham. 2021 · 2021
Cited alongside, same era.
Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans. 2021 · 2021
Cited alongside, same era.
Perceiver: General perception with iterative attention
Andrew Jaegle, Felix Gimeno, Andy Brock, Oriol Vinyals, Andrew Zisserman, and Joao Carreira. 2021 · 2021
Cited alongside, same era.
Generative models as a data source for multiview representation learning
Ali Jahanian, Xavier Puig, Yonglong Tian, and Phillip Isola. 2021 · 2021
Cited alongside, same era.
Diffusion models already have a semantic latent space
Mingi Kwon, Jaeseok Jeong, and Youngjung Uh. 2022 · 2022
Later among the works it cites.
xformers: A modular and hackable transformer modelling library
Benjamin Lefaudeux, Francisco Massa, Diana Liskovich, Wenhan Xiong, Vittorio Caggiano, Sean Naren, Min Xu, Jieru Hu, Marta Tintore, Susan Zhang, Patrick Labatut, and Daniel Haziza. 2022 · 2022
Later among the works it cites.
Fine-grained image editing by pixel-wise guidance using diffusion models
Naoki Matsunaga, Masato Ishii, Akio Hayakawa, Kenji Suzuki, and Takuya Narihira. 2022 · 2022
Later among the works it cites.
Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. 2022 · 2022
Later among the works it cites.
Photorealistic text-to-image diffusion models with deep language understanding
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Scaling up visual and vision-language representation learning with noisy text supervision
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. 2021 · 2021
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
Cited alongside, same era.
P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks
Xiao Liu, Kaixuan Ji, Yicheng Fu, Zhengxiao Du, Zhilin Yang, and Jie Tang. 2021 · 2021
Cited alongside, same era.
Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Alexander Quinn Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. 2021 · 2021
Cited alongside, same era.
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, et al. 2021 · 2021
Cited alongside, same era.
Multimodal few-shot learning with frozen language models
Maria Tsimpoukelli, Jacob L Menick, Serkan Cabi, SM Eslami, Oriol Vinyals, and Felix Hill. 2021 · 2021
Cited alongside, same era.
Filip: Fine-grained interactive language-image pre-training
Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu, Hang Xu, Xiaodan Liang, Zhenguo Li, Xin Jiang, and Chunjing Xu. 2021 · 2021
Cited alongside, same era.
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S. Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho, David J Fleet, and Mohammad Norouzi. 2022 · 2022
Later among the works it cites.
Laion-5b: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al. 2022 · 2022
Later among the works it cites.
What the daam: Interpreting stable diffusion using cross attention
Raphael Tang, Akshat Pandey, Zhiying Jiang, Gefei Yang, Karun Kumar, Jimmy Lin, and Ferhan Ture. 2022 · 2022
Later among the works it cites.
Winoground: Probing vision and language models for visio-linguistic compositionality
Tristan Thrush, Ryan Jiang, Max Bartolo, Amanpreet Singh, Adina Williams, Douwe Kiela, and Candace Ross. 2022 · 2022
Later among the works it cites.
Peng Wang, An Yang, Rui Men, Junyang Lin, Shuai Bai, Zhikang Li, Jianxin Ma, Chang Zhou, Jingren Zhou, and Hongxia Yang. 2022 · 2022
Later among the works it cites.
Diffusion models for implicit image segmentation ensembles
Julia Wolleb, Robin Sandkühler, Florentin Bieder, Philippe Valmaggia, and Philippe C Cattin. 2022 · 2022
Later among the works it cites.
Vl-checklist: Evaluating pre-trained vision-language models with objects, attributes and relations
Tiancheng Zhao, Tianqi Zhang, Mingwei Zhu, Haozhan Shen, Kyusong Lee, Xiaopeng Lu, and Jianwei Yin. 2022 · 2022
Later among the works it cites.
Conditional prompt learning for vision-language models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. 2022 · 2022
Later among the works it cites.
Text-to-image diffusion models are zero-shot classifiers
Kevin Clark and Priyank Jaini. 2023 · 2023
Closest in time.
Unlocking spatial comprehension in text-to-image diffusion models
Mohammad Mahdi Derakhshani, Menglin Xia, Harkirat Behl, Cees GM Snoek, and Victor Rühle. 2023 · 2023
Closest in time.
Latent consistency models: Synthesizing high-resolution images with few-step inference
Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao. 2023 · 2023
Closest in time.
Selfeval: Leveraging the discriminative nature of generative models for evaluation
Sai Saketh Rambhatla and Ishan Misra. 2023 · 2023
Closest in time.
Diffusion models as masked autoencoders
Chen Wei, Karttikeya Mangalam, Po-Yao Huang, Yanghao Li, Haoqi Fan, Hu Xu, Huiyu Wang, Cihang Xie, Alan Yuille, and Christoph Feichtenhofer. 2023 · 2023
Closest in time.
Harnessing the spatial-temporal attention of diffusion models for high-fidelity text-to-image synthesis
Qiucheng Wu, Yujian Liu, Handong Zhao, Trung Bui, Zhe Lin, Yang Zhang, and Shiyu Chang. 2023 · 2023
Closest in time.
Visual instruction tuning
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2024 · 2024
Closest in time.
What you see is what you read? improving text-image alignment evaluation
Michal Yarom, Yonatan Bitton, Soravit Changpinyo, Roee Aharoni, Jonathan Herzig, Oran Lang, Eran Ofek, and Idan Szpektor. 2024 · 2024
Closest in time.