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Recent years have witnessed a rapid growth of deep generative models, with text-to-image models gaining significant attention from the public.
A naturalistic open source movie for optical flow evaluation
D. J. Butler, J. Wulff, G. B. Stanley, and M. J. Black · 2012
Earlier work this paper cites.
AVA: A large-scale database for aesthetic visual analysis
Naila Murray, Luca Marchesotti, and Florent Perronnin · 2012
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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 S. Bernstein, Alexander C. Berg, and Li Fei-Fei · 2014
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
Earlier work this paper cites.
Flownet: Learning optical flow with convolutional networks
A. Dosovitskiy, P. Fischer, E. Ilg, P. Häusser, C. Hazırbaş, V. Golkov, P. v.d. Smagt, D. Cremers, and T. Brox · 2015
Earlier work this paper cites.
Draw: A recurrent neural network for image generation
Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Rezende, and Daan Wierstra · 2015
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Generating images from captions with attention
Elman Mansimov, Emilio Parisotto, Jimmy Lei Ba, and Ruslan Salakhutdinov · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Rethinking the Inception Architecture for Computer Vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna · 2015
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A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
N. Mayer, E. Ilg, P. Häusser, P. Fischer, D. Cremers, A. Dosovitskiy, and T. Brox · 2016
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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 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
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DQN-TAMER: Human-in-the-Loop Reinforcement Learning with Intractable Feedback
Riku Arakawa, Sosuke Kobayashi, Yuya Unno, Yuta Tsuboi, and Shin ichi Maeda · 2018
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Shane T. Barratt and Rishi Sharma · 2018
Earlier work this paper cites.
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix Wichmann, and Wieland Brendel · 2018
Cited alongside, same era.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Raft: Recurrent all-pairs field transforms for optical flow
Zachary Teed and Jia Deng · 2020
Cited alongside, same era.
LoRA: Low-Rank Adaptation of Large Language Models, 2021
Edward Hu, Yelong Shen, Phil Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Lu Wang, and Weizhu Chen · 2021
Cited alongside, same era.
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
Later among the works it cites.
Simulacra Aesthetic Captions
John David Pressman, Katherine Crowson, and Simulacra Captions Contributors · 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
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High-Resolution Image Synthesis with Latent Diffusion Models
Robin Rombach, A. Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Later among the works it cites.
Photorealistic text-to-image diffusion models with deep language understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al · 2022
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Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen · 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, Gretchen Krueger, and Ilya Sutskever · 2021
Cited alongside, same era.
Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
Cited alongside, same era.
ERNIE-ViLG: Unified Generative Pre-training for Bidirectional Vision-Language Generation
Han Zhang, Weichong Yin, Yewei Fang, Lanxin Li, Boqiang Duan, Zhihua Wu, Yu Sun, Hao Tian, Hua Wu, and Haifeng Wang · 2021
Cited alongside, same era.
Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesis
Weixi Feng, Xuehai He, Tsu-Jui Fu, Varun Jampani, Arjun Reddy Akula, P. Narayana, Sugato Basu, Xin Eric Wang, and William Yang Wang · 2022
Cited alongside, same era.
Optimizing Prompts for Text-to-Image Generation
Yaru Hao, Zewen Chi, Li Dong, and Furu Wei · 2022
Cited alongside, same era.
Classifier-Free Diffusion Guidance
Jonathan Ho · 2022
Cited alongside, same era.
CLIP+MLP Aesthetic Score Predictor
Christoph Schuhmann · 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
Later among the works it cites.
DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models
Zijie J. Wang, Evan Montoya, David Munechika, Haoyang Yang, Benjamin Hoover, and Duen Horng Chau · 2022
Later among the works it cites.
Instructpix2pix: Learning to follow image editing instructions
Tim Brooks, Aleksander Holynski, and Alexei A Efros · 2023
Closest in time.
ERNIE-ViLG 2.0: Improving text-to-image diffusion model with knowledge-enhanced mixture-of-denoising-experts
Zhida Feng, Zhenyu Zhang, Xintong Yu, Yewei Fang, Lanxin Li, Xuyi Chen, Yuxiang Lu, Jiaxiang Liu, Weichong Yin, Shikun Feng, et al · 2023
Closest in time.
Aligning Text-to-Image Models using Human Feedback
Kimin Lee, Hao Liu, Moonkyung Ryu, Olivia Watkins, Yuqing Du, Craig Boutilier, P. Abbeel, Mohammad Ghavamzadeh, and Shixiang Shane Gu · 2023
Closest in time.
Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation
Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman · 2023
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Videoflow: Exploiting temporal cues for multi-frame optical flow estimation
Xiaoyu Shi, Zhaoyang Huang, Weikang Bian, Dasong Li, Manyuan Zhang, Ka Chun Cheung, Simon See, Hongwei Qin, Jifeng Dai, and Hongsheng Li · 2023
Closest in time.
Flowformer++: Masked cost volume autoencoding for pretraining optical flow estimation
Xiaoyu Shi, Zhaoyang Huang, Dasong Li, Manyuan Zhang, Ka Chun Cheung, Simon See, Hongwei Qin, Jifeng Dai, and Hongsheng Li · 2023
Closest in time.
ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation
Yuxiang Wei, Yabo Zhang, Zhilong Ji, Jinfeng Bai, Lei Zhang, and Wangmeng Zuo · 2023
Closest in time.