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Video prediction is a challenging task.
Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition
Kunihiko Fukushima and Sei Miyake · 1982
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
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Recognizing human actions: a local svm approach
Christian Schuldt, Ivan Laptev, and Barbara Caputo · 2004
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Adversarial score matching and improved sampling for image generation
Alexia Jolicoeur-Martineau, Rémi Piché-Taillefer, Rémi Tachet des Combes, and Ioannis Mitliagkas · 2009
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2010
Earlier work this paper cites.
Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2011
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Ucf101: A dataset of 101 human actions classes from videos in the wild
Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah · 2012
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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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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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Efficient object localization using convolutional networks
Jonathan Tompson, Ross Goroshin, Arjun Jain, Yann LeCun, and Christoph Bregler · 2015
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Unsupervised learning of video representations using lstms
Nitish Srivastava, Elman Mansimov, and Ruslan Salakhudinov · 2015
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The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
Earlier work this paper cites.
Recombinator networks: Learning coarse-to-fine feature aggregation
Sina Honari, Jason Yosinski, Pascal Vincent, and Christopher Pal · 2016
Earlier work this paper cites.
Long short-term memory-networks for machine reading
Jianpeng Cheng, Li Dong, and Mirella Lapata · 2016
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Generating videos with scene dynamics
Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba · 2016
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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen, and Xi Chen · 2016
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Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P Kingma · 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
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Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al · 2017
Earlier work this paper cites.
Decomposing motion and content for natural video sequence prediction
Ruben Villegas, Jimei Yang, Seunghoon Hong, Xunyu Lin, and Honglak Lee · 2017
Earlier work this paper cites.
Temporal generative adversarial nets with singular value clipping
Masaki Saito, Eiichi Matsumoto, and Shunta Saito · 2017
Earlier work this paper cites.
Video frame interpolation via adaptive convolution
Simon Niklaus, Long Mai, and Feng Liu · 2017
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Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor · 2017
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Self-supervised visual planning with temporal skip connections
Frederik Ebert, Chelsea Finn, Alex X Lee, and Sergey Levine · 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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Flexible spatio-temporal networks for video prediction
Chaochao Lu, Michael Hirsch, and Bernhard Scholkopf · 2017
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Group normalization
Yuxin Wu and Kaiming He · 2018
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Stochastic video generation with a learned prior
Emily Denton and Rob Fergus · 2018
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Stochastic adversarial video prediction
Alex X. Lee, Richard Zhang, Frederik Ebert, Pieter Abbeel, Chelsea Finn, and Sergey Levine · 2018
Cited alongside, same era.
Towards accurate generative models of video: A new metric & challenges
Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphael Marinier, Marcin Michalski, and Sylvain Gelly · 2018
Cited alongside, same era.
Mocogan: Decomposing motion and content for video generation
Ruslan Rakhimov, Denis Volkhonskiy, Alexey Artemov, Denis Zorin, and Evgeny Burnaev · 2020
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Train sparsely, generate densely: Memory-efficient unsupervised training of high-resolution temporal gan
Masaki Saito, Shunta Saito, Masanori Koyama, and Sosuke Kobayashi · 2020
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Transformation-based adversarial video prediction on large-scale data
Pauline Luc, Aidan Clark, Sander Dieleman, Diego de Las Casas, Yotam Doron, Albin Cassirer, and Karen Simonyan · 2020
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Learning for video compression with recurrent auto-encoder and recurrent probability model
Ren Yang, Fabian Mentzer, Luc Van Gool, and Radu Timofte · 2020
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Input dropout for spatially aligned modalities
Sébastien de Blois, Mathieu Garon, Christian Gagné, and Jean-François Lalonde · 2020
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Sergey Tulyakov, Ming-Yu Liu, Xiaodong Yang, and Jan Kautz · 2018
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Sandra Aigner and Marco Körner · 2018
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Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
Cited alongside, same era.
Super slomo: High quality estimation of multiple intermediate frames for video interpolation
Huaizu Jiang, Deqing Sun, Varun Jampani, Ming-Hsuan Yang, Erik Learned-Miller, and Jan Kautz · 2018
Cited alongside, same era.
Scaling autoregressive video models
Dirk Weissenborn, Oscar Täckström, and Jakob Uszkoreit · 2019
Cited alongside, same era.
High fidelity video prediction with large stochastic recurrent neural networks
Ruben Villegas, Arkanath Pathak, Harini Kannan, Dumitru Erhan, Quoc V Le, and Honglak Lee · 2019
Cited alongside, same era.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
Cited alongside, same era.
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Stochastic dynamics for video infilling
Qiangeng Xu, Hanwang Zhang, Weiyue Wang, Peter Belhumeur, and Ulrich Neumann · 2020
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Exploring spatial-temporal multi-frequency analysis for high-fidelity and temporal-consistency video prediction
Beibei Jin, Yu Hu, Qiankun Tang, Jingyu Niu, Zhiping Shi, Yinhe Han, and Xiaowei Li · 2020
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Greedy hierarchical variational autoencoders for large-scale video prediction
Bohan Wu, Suraj Nair, Roberto Martin-Martin, Li Fei-Fei, and Chelsea Finn · 2021
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Fitvid: Overfitting in pixel-level video prediction
Mohammad Babaeizadeh, Mohammad Taghi Saffar, Suraj Nair, Sergey Levine, Chelsea Finn, and Dumitru Erhan · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Denoising diffusion gamma models
Eliya Nachmani, Robin San Roman, and Lior Wolf · 2021
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Palette: Image-to-image diffusion models
Chitwan Saharia, William Chan, Huiwen Chang, Chris A Lee, Jonathan Ho, Tim Salimans, David J Fleet, and Mohammad Norouzi · 2021
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Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen · 2021
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Slamp: Stochastic latent appearance and motion prediction
Adil Kaan Akan, Erkut Erdem, Aykut Erdem, and Fatma Güney · 2021
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Videogpt: Video generation using vq-vae and transformers
Wilson Yan, Yunzhi Zhang, Pieter Abbeel, and Aravind Srinivas · 2021
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Ccvs: Context-aware controllable video synthesis
Guillaume Le Moing, Jean Ponce, and Cordelia Schmid · 2021
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Temporal shift gan for large scale video generation
Andres Munoz, Mohammadreza Zolfaghari, Max Argus, and Thomas Brox · 2021
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Accurate grid keypoint learning for efficient video prediction
Xiaojie Gao, Yueming Jin, Qi Dou, Chi-Wing Fu, and Pheng-Ann Heng · 2021
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Progressive distillation for fast sampling of diffusion models
Tim Salimans and Jonathan Ho · 2022
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Pseudo numerical methods for diffusion models on manifolds
Luping Liu, Yi Ren, Zhijie Lin, and Zhou Zhao · 2022
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Tackling the generative learning trilemma with denoising diffusion GANs
Zhisheng Xiao, Karsten Kreis, and Arash Vahdat · 2022
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Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J. Fleet · 2022
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SDEdit: Guided image synthesis and editing with stochastic differential equations
Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon · 2022
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Denoising diffusion restoration models
Bahjat Kawar, Michael Elad, Stefano Ermon, and Jiaming Song · 2022
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Diffusion probabilistic modeling for video generation
Ruihan Yang, Prakhar Srivastava, and Stephan Mandt · 2022
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Generating videos with dynamics-aware implicit generative adversarial networks
Sihyun Yu, Jihoon Tack, Sangwoo Mo, Hyunsu Kim, Junho Kim, Jung-Woo Ha, and Jinwoo Shin · 2022
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