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Video understanding calls for a model to learn the characteristic interplay between static scene content and its dynamics: Given an image, the model must be able to predict a future progression of the portrayed scene and, conversely, a video should be explained in terms of its static image content and all the remaining characteristics not present in the initial frame.
Prediction, cognition and the brain
Andreja Bubic, D Yves von Cramon, and Ricarda I Schubotz · 2010
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Generative adversarial networks
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Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge · 2015
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Autoencoding beyond pixels using a learned similarity metric
Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, and Ole Winther · 2016
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Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna · 2016
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João Carreira and Andrew Zisserman · 2017
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Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2017
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Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
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Frederik Ebert, Chelsea Finn, Alex X. Lee, and Sergey Levine · 2017
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Chelsea Finn and Sergey Levine · 2017
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Ishaan Gulrajani, Faruk Ahmed, Martín Arjovsky, Vincent Dumoulin, and Aaron C. Courville · 2017
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GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
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Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros · 2017
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The Kinetics human action video dataset
Will Kay, João Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, Mustafa Suleyman, and Andrew Zisserman · 2017
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Geometric GAN
Jae Hyun Lim and Jong Chul Ye · 2017
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Unsupervised video understanding by reconciliation of posture similarities
Timo Milbich, Miguel Ángel Bautista, Ekaterina Sutter, and Björn Ommer · 2017
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Decomposing motion and content for natural video sequence prediction
Ruben Villegas, Jimei Yang, Seunghoon Hong, Xunyu Lin, and Honglak Lee · 2017
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The pose knows: Video forecasting by generating pose futures
Jacob Walker, Kenneth Marino, Abhinav Gupta, and Martial Hebert · 2017
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Synthesizing dynamic patterns by spatial-temporal generative convnet
Jianwen Xie, Song-Chun Zhu, and Ying Nian Wu · 2017
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InfoVAE: Information maximizing variational autoencoders
Shengjia Zhao, Jiaming Song, and Stefano Ermon · 2017
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Jun-Yan Zhu, Richard Zhang, Deepak Pathak, Trevor Darrell, Alexei A. Efros, Oliver Wang, and Eli Shechtman · 2017
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Stochastic variational video prediction
Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy H. Campbell, and Sergey Levine · 2018
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Drive&act: A multi-modal dataset for fine-grained driver behavior recognition in autonomous vehicles
Manuel Martin, Alina Roitberg, Monica Haurilet, Matthias Horne, Simon Reiß, Michael Voit, and Rainer Stiefelhagen · 2019
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Unsupervised learning of object structure and dynamics from videos
Matthias Minderer, Chen Sun, Ruben Villegas, Forrester Cole, Kevin P. Murphy, and Honglak Lee · 2019
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Normalizing flows for probabilistic modeling and inference
George Papamakarios, Eric T. Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan · 2019
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Patrick Esser, Johannes Haux, Timo Milbich, and Björn Ommer · 2018
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A new large scale dynamic texture dataset with application to convnet understanding
Isma Hadji and Richard P. Wildes · 2018
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i-revnet: Deep invertible networks
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Glow: Generative flow with invertible 1x1 convolutions
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Stochastic adversarial video prediction
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Which training methods for gans do actually converge?
Lars M. Mescheder, Andreas Geiger, and Sebastian Nowozin · 2018
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Semantic image synthesis with spatially-adaptive normalization
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
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Animating arbitrary objects via deep motion transfer
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First order motion model for image animation
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Learning Dynamic Generator Model by Alternating Back-Propagation through Time
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Unsupervised magnification of posture deviations across subjects
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A disentangling invertible interpretation network for explaining latent representations
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Stochastic latent residual video prediction
Jean-Yves Franceschi, Edouard Delasalles, Mickaël Chen, Sylvain Lamprier, and Patrick Gallinari · 2020
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Normalizing flows: An introduction and review of current methods
Ivan Kobyzev, Simon Prince, and Marcus Brubaker · 2020
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Videoflow: A conditional flow-based model for stochastic video generation
Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan, Chelsea Finn, Sergey Levine, Laurent Dinh, and Durk Kingma · 2020
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DeepLandscape: Adversarial Modeling of Landscape Videos
Elizaveta Logacheva, Roman Suvorov, Oleg Khomenko, Anton Mashikhin, and Victor Lempitsky · 2020
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SRFlow: Learning the super-resolution space with normalizing flow
Andreas Lugmayr, Martin Danelljan, Luc Van Gool, and Radu Timofte · 2020
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Detecting distraction of drivers using convolutional neural network
Sarfaraz Masood, Abhinav Rai, Aakash Aggarwal, Mohammad Najam Doja, and Musheer Ahmad · 2020
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Keyframing the future: Keyframe discovery for visual prediction and planning
Karl Pertsch, Oleh Rybkin, Jingyun Yang, Shenghao Zhou, Konstantinos G. Derpanis, Kostas Daniilidis, Joseph J. Lim, and Andrew Jaegle · 2020
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C-Flow: Conditional Generative Flow Models for Images and 3D Point Clouds
Albert Pumarola, Stefan Popov, Francesc Moreno-Noguer, and Vittorio Ferrari · 2020
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Making Sense of CNNs: Interpreting Deep Representations and Their Invariances with INNs
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Network-to-network translation with conditional invertible neural networks
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Scaling autoregressive video models
Dirk Weissenborn, Oscar Täckström, and Jakob Uszkoreit · 2020
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Cooperative training of descriptor and generator networks
Jianwen Xie, Yang Lu, Ruiqi Gao, Song-Chun Zhu, and Ying Nian Wu · 2020
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Wavelet flow: Fast training of high resolution normalizing flows
Jason J. Yu, Konstantinos G. Derpanis, and Marcus A. Brubaker · 2020
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DTVNet: Dynamic Time-Lapse Video Generation via Single Still Image
Jiangning Zhang, Chao Xu, Liang Liu, Mengmeng Wang, Xia Wu, Yong Liu, and Yunliang Jiang · 2020
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Behavior-driven synthesis of human dynamics
Andreas Blattmann, Timo Milbich, Michael Dorkenwald, and Bjorn Ommer · 2021
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Unsupervised behaviour analysis and magnification (ubam) using deep learning
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