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Learning generative object models from unlabelled videos is a long standing problem and required for causal scene modeling.
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Judea Pearl · 2009
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Thomas G. Bever and David Poeppel · 2010
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Object Segmentation by Long Term Analysis of Point Trajectories
Thomas Brox and Jitendra Malik · 2010
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Weakly supervised learning of foreground-background segmentation using masked RBMs
Nicolas Heess, Nicolas Le Roux, and John Winn · 2011
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Learning a generative model of images by factoring appearance and shape
Nicolas Le Roux, Nicolas Heess, Jamie Shotton, and John Winn · 2011
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Higher order motion models and spectral clustering
Peter Ochs and Thomas Brox · 2012
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BGSLibrary: An OpenCV C++ background subtraction library
Andrews Sobral · 2013
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Generative adversarial nets
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Auto-encoding variational Bayes
Diederik P. Kingma and Max Welling · 2014
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Recurrent models of visual attention
Volodymyr Mnih, Nicolas Heess, Alex Graves, et al · 2014
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Segmentation of moving objects by long term video analysis
Peter Ochs, Jitendra Malik, and Thomas Brox · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Improving background subtraction using local binary similarity patterns
Pierre-Luc St-Charles and Guillaume-Alexandre Bilodeau · 2014
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DRAW: A recurrent neural network for image generation
Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Rezende, and Daan Wierstra · 2015
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The informed sampler: A discriminative approach to Bayesian inference in generative computer vision models
Varun Jampani, Sebastian Nowozin, Matthew Loper, and Peter V. Gehler · 2015
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Motion Trajectory Segmentation via Minimum Cost Multicuts
Margret Keuper, Bjoern Andres, and Thomas Brox · 2015
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Picture: A probabilistic programming language for scene perception
T. D. Kulkarni, P. Kohli, J. B. Tenenbaum, and V. Mansinghka · 2015
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Generative modeling of infinite occluded objects for compositional scene representation
Jinyang Yuan, Bin Li, and Xiangyang Xue · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
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Attend, Infer, Repeat: Fast Scene Understanding with Generative Models
S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, Koray Kavukcuoglu, and Geoffrey E Hinton · 2016
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A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation
Nikolaus Mayer, Eddy Ilg, Philip Hausser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox · 2016
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Universal background subtraction using word consensus models
Pierre-Luc St-Charles, Guillaume-Alexandre Bilodeau, and Robert Bergevin · 2016
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Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor · 2017
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Neural expectation maximization
Klaus Greff, Sjoerd van Steenkiste, and Jürgen Schmidhuber · 2017
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Beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
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FlowNet 2.0: Evolution of Optical Flow Estimation With Deep Networks
Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, and Thomas Brox · 2017
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Learning Features by Watching Objects Move
Deepak Pathak, Ross Girshick, Piotr Dollar, Trevor Darrell, and Bharath Hariharan · 2017
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Unsupervised Moving Object Detection via Contextual Information Separation
Yanchao Yang, Antonio Loquercio, Davide Scaramuzza, and Stefano Soatto · 2019
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CLEVRER: Collision events for video representation and reasoning
Kexin Yi, Chuang Gan, Yunzhu Li, Pushmeet Kohli, Jiajun Wu, Antonio Torralba, and Joshua B Tenenbaum · 2019
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Learning to infer 3d object models from images
Chang Chen, Fei Deng, and Sungjin Ahn · 2020
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Exploiting Spatial Invariance for Scalable Unsupervised Object Tracking
Eric Crawford and Joelle Pineau · 2020
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RELATE: Physically Plausible Multi-Object Scene Synthesis
Sebastien Ehrhardt, Oliver Groth, Aron Monszpart, Martin Engelcke, Ingmar Posner, Niloy Mitra, and Andrea Vedaldi · 2020
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J. Peters, D. Janzing, and B. Schölkopf · 2017
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The 2017 DAVIS challenge on video object segmentation
Jordi Pont-Tuset, Federico Perazzi, Sergi Caelles, Pablo Arbeláez, Alex Sorkine-Hornung, and Luc Van Gool · 2017
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Neural scene de-rendering
Jiajun Wu, Joshua B Tenenbaum, and Pushmeet Kohli · 2017
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Lr-GAN: Layered recursive generative adversarial networks for image generation
Jianwei Yang, Anitha Kannan, Dhruv Batra, and Devi Parikh · 2017
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A guide to convolution arithmetic for deep learning
Vincent Dumoulin and Francesco Visin · 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
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Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects
Adam Kosiorek, Hyunjik Kim, Yee Whye Teh, and Ingmar Posner · 2018
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GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations
Martin Engelcke, Adam R. Kosiorek, Oiwi Parker Jones, and Ingmar Posner · 2020
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On the Binding Problem in Artificial Neural Networks
Klaus Greff, Sjoerd van Steenkiste, and Jürgen Schmidhuber · 2020
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Analyzing and Improving the Image Quality of StyleGAN
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2020
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Learning by Analogy: Reliable Supervision From Transformations for Unsupervised Optical Flow Estimation
Liang Liu, Jiangning Zhang, Ruifei He, Yong Liu, Yabiao Wang, Ying Tai, Donghao Luo, Chengjie Wang, Jilin Li, and Feiyue Huang · 2020
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Object-Centric Learning with Slot Attention
Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, and Thomas Kipf · 2020
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Learning object-centric representations of multi-object scenes from multiple views
Li Nanbo, Cian Eastwood, and Robert Fisher · 2020
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BlockGAN: Learning 3d object-aware scene representations from unlabelled images
Thu Nguyen-Phuoc, Christian Richardt, Long Mai, Yong-Liang Yang, and Niloy Mitra · 2020
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Pytorch-vae
A.K Subramanian · 2020
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RAFT: Recurrent All-Pairs Field Transforms for Optical Flow
Zachary Teed and Jia Deng · 2020
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NVAE: A Deep Hierarchical Variational Autoencoder
Arash Vahdat and Jan Kautz · 2020
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Towards causal generative scene models via competition of experts
Julius von Kügelgen, Ivan Ustyuzhaninov, Peter Gehler, Matthias Bethge, and Bernhard Schölkopf · 2020
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Unmasking the inductive biases of unsupervised object representations for video sequences
Marissa A Weis, Kashyap Chitta, Yash Sharma, Wieland Brendel, Matthias Bethge, Andreas Geiger, and Alexander S Ecker · 2020
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GENESIS-V2: Inferring unordered object representations without iterative refinement
Martin Engelcke, Oiwi Parker Jones, and Ingmar Posner · 2021
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ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation
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GIRAFFE: Representing scenes as compositional generative neural feature fields
Michael Niemeyer and Andreas Geiger · 2021
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Toward causal representation learning
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cc3d: Connected components on multilabel 3D images
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Self-supervised Video Object Segmentation by Motion Grouping
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Discovering Objects That Can Move
Zhipeng Bao, Pavel Tokmakov, Allan Jabri, Yu-Xiong Wang, Adrien Gaidon, and Martial Hebert · 2022
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Kubric: A scalable dataset generator
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Conditional Object-Centric Learning from Video
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