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Advances in unsupervised learning of object-representations have culminated in the development of a broad range of methods for unsupervised object segmentation and interpretable object-centric scene generation.
Comparing Partitions
Lawrence Hubert and Phipps Arabie · 1985
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Long Short-Term Memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Contour Detection and Hierarchical Image Segmentation
Pablo Arbelaez, Michael Maire, Charless Fowlkes, and Jitendra Malik · 2010
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Are We Ready for Autonomous Driving? The KITTI Vision Benchmark Suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
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Auto-Encoding Variational Bayes
Diederik P Kingma and Max Welling · 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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Generative Adversarial Nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Efficient Inference in Occlusion-Aware Generative models of Images
Jonathan Huang and Kevin Murphy · 2015
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Spatial Transformer Networks
Max Jaderberg, Karen Simonyan, Andrew Zisserman, and Koray Kavukcuoglu · 2015
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U-Net: Convolutional Networks for Biomedical Image Segmentation
Olaf Ronneberger, Philipp Fischer, 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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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
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Attend, Infer, Repeat: Fast Scene Understanding with Generative Models
SM Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, Geoffrey E Hinton, et al · 2016
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Tagger: Deep Unsupervised Perceptual Grouping
Klaus Greff, Antti Rasmus, Mathias Berglund, Tele Hao, Harri Valpola, and Jürgen Schmidhuber · 2016
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Instance Normalization: The Missing Ingredient for Fast Stylization
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2016
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Mask R-CNN
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Neural Expectation Maximization
Klaus Greff, Sjoerd van Steenkiste, and Jürgen Schmidhuber · 2017
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Semantic Instance Segmentation via Deep Metric Learning
Alireza Fathi, Zbigniew Wojna, Vivek Rathod, Peng Wang, Hyun Oh Song, Sergio Guadarrama, and Kevin P Murphy · 2017
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Deep Watershed Transform for Instance Segmentation
Min Bai and Raquel Urtasun · 2017
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Semantic Instance Segmentation with a Discriminative Loss Function
Bert De Brabandere, Davy Neven, and Luc Van Gool · 2017
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Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challenge
Andy Zeng, Kuan-Ting Yu, Shuran Song, Daniel Suo, Ed Walker Jr, Alberto Rodriguez, and Jianxiong Xiao · 2017
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Stick-Breaking Variational Autoencoders
Eric Nalisnick and Padhraic Smyth · 2017
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Deep Sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan Salakhutdinov, and Alexander Smola · 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
Cited alongside, same era.
Automatic Differentiation in PyTorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Cited alongside, same era.
Deep Object-Centric Representations for Generalizable Robot Learning
Multi-Object Datasets, 2019
Rishabh Kabra, Chris Burgess, Loic Matthey, Raphael Lopez Kaufman, Klaus Greff, Malcolm Reynolds, and Alexander Lerchner · 2019
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Large Scale GAN Training for High Fidelity Natural Image Synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
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Deep Set Prediction Networks
Yan Zhang, Jonathon Hare, and Adam Prügel-Bennett · 2019
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On the Limitations of Representing Functions on Sets
Edward Wagstaff, Fabian Fuchs, Martin Engelcke, Ingmar Posner, and Michael A Osborne · 2019
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Spatial Broadcast Decoder: A Simple Architecture for Learning Disentangled Representations in VAEs
Nicholas Watters, Loic Matthey, Christopher P Burgess, and Alexander Lerchner · 2019
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SPACE: Unsupervised Object-Oriented Scene Representation via Spatial Attention and Decomposition
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Coline Devin, Pieter Abbeel, Trevor Darrell, and Sergey Levine · 2018
Cited alongside, same era.
Multi-Objects Generation with Amortized Structural Regularization
Kun Xu, Chongxuan Li, Jun Zhu, and Bo Zhang · 2018
Cited alongside, same era.
Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects
Adam Kosiorek, Hyunjik Kim, Yee Whye Teh, and Ingmar Posner · 2018
Cited alongside, same era.
Semi-Convolutional Operators for Instance Segmentation
David Novotny, Samuel Albanie, Diane Larlus, and Andrea Vedaldi · 2018
Cited alongside, same era.
ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking
Oliver Groth, Fabian B Fuchs, Ingmar Posner, and Andrea Vedaldi · 2018
Cited alongside, same era.
Recurrent Pixel Embedding for Instance Grouping
Shu Kong and Charless C Fowlkes · 2018
Cited alongside, same era.
Danilo Jimenez Rezende and Fabio Viola · 2018
Cited alongside, same era.
Zhixuan Lin, Yi-Fu Wu, Skand Vishwanath Peri, Weihao Sun, Gautam Singh, Fei Deng, Jindong Jiang, and Sungjin Ahn · 2020
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SCALOR: Generative World Models with Scalable Object Representations
Jindong Jiang, Sepehr Janghorbani, Gerard De Melo, and Sungjin Ahn · 2020
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Generative Neurosymbolic Machines
Jindong Jiang and Sungjin Ahn · 2020
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Entity Abstraction in Visual Model-Based Reinforcement Learning
Rishi Veerapaneni, John D Co-Reyes, Michael Chang, Michael Janner, Chelsea Finn, Jiajun Wu, Joshua Tenenbaum, and Sergey Levine · 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 to Manipulate Individual Objects in an Image
Yanchao Yang, Yutong Chen, and Stefano Soatto · 2020
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Learning Physical Graph Representations from Visual Scenes
Daniel M Bear, Chaofei Fan, Damian Mrowca, Yunzhu Li, Seth Alter, Aran Nayebi, Jeremy Schwartz, Li Fei-Fei, Jiajun Wu, Joshua B Tenenbaum, et al · 2020
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Object-Centric Image Generation with Factored Depths, Locations, and Appearances
Titas Anciukevicius, Christoph H Lampert, and Paul Henderson · 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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RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces
Sebastien Ehrhardt, Oliver Groth, Aron Monszpart, Martin Engelcke, Ingmar Posner, Niloy Mitra, and Andrea Vedaldi · 2020
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GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields
Michael Niemeyer and Andreas Geiger · 2020
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Scaling Data-Driven Robotics with Reward Sketching and Batch Reinforcement Learning
Serkan Cabi, Sergio Gómez Colmenarejo, Alexander Novikov, Ksenia Konyushkova, Scott Reed, Rae Jeong, Konrad Zolna, Yusuf Aytar, David Budden, Mel Vecerik, et al · 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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pytorch-fid: FID Score for PyTorch
Maximilian Seitzer · 2020
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NVAE: A Deep Hierarchical Variational Autoencoder
Arash Vahdat and Jan Kautz · 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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