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We study the problem of inferring an object-centric scene representation from a single image, aiming to derive a representation that explains the image formation process, captures the scene's 3D nature, and is learned without supervision.
Optical models for direct volume rendering
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Image quality assessment: From error visibility to structural similarity
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Discovering objects and their location in images
Josef Sivic, Bryan C Russell, Alexei A Efros, Andrew Zisserman, and William T Freeman · 2005
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Unsupervised learning of categories from sets of partially matching image features
Kristen Grauman and Trevor Darrell · 2006
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Using multiple segmentations to discover objects and their extent in image collections
Bryan C Russell, William T Freeman, Alexei A Efros, Josef Sivic, and Andrew Zisserman · 2006
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Unsupervised discovery of visual object class hierarchies
Josef Sivic, Bryan C Russell, Andrew Zisserman, William T Freeman, and Alexei A Efros · 2008
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Discriminative clustering for image co-segmentation
Armand Joulin, Francis Bach, and Jean Ponce · 2010
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Object cosegmentation
Sara Vicente, Carsten Rother, and Vladimir Kolmogorov · 2011
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Unsupervised co-segmentation through region matching
Jose C Rubio, Joan Serrat, Antonio López, and Nikos Paragios · 2012
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Unsupervised object class discovery via saliency-guided multiple class learning
Jun-Yan Zhu, Jiajun Wu, Yichen Wei, Eric Chang, and Zhuowen Tu · 2012
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Unsupervised joint object discovery and segmentation in internet images
Michael Rubinstein, Armand Joulin, Johannes Kopf, and Ce Liu · 2013
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
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Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Shapenet: an information-rich 3d model repository
Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al · 2015
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Unsupervised object discovery and localization in the wild: Part-based matching with bottom-up region proposals
Minsu Cho, Suha Kwak, Cordelia Schmid, and Jean Ponce · 2015
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Effective approaches to attention-based neural machine translation
Minh-Thang Luong, Hieu Pham, and Christopher D Manning · 2015
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Attend, infer, repeat: Fast scene understanding with generative models
SM Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, Koray Kavukcuoglu, and Geoffrey E Hinton · 2016
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Tagger: Deep unsupervised perceptual grouping
Klaus Greff, Antti Rasmus, Mathias Berglund, Tele Hotloo Hao, Jürgen Schmidhuber, and Harri Valpola · 2016
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Perceptual losses for real-time style transfer and super-resolution
Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
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Neural expectation maximization
Klaus Greff, Sjoerd Van Steenkiste, and Jürgen Schmidhuber · 2017
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Clevr: a diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens Van Der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick · 2017
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Neural scene de-rendering
Jiajun Wu, Joshua B Tenenbaum, and Pushmeet Kohli · 2017
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Neural scene representation and rendering
SM Ali Eslami, Danilo Jimenez Rezende, Frederic Besse, Fabio Viola, Ari S Morcos, Marta Garnelo, Avraham Ruderman, Andrei A Rusu, Ivo Danihelka, Karol Gregor, et al · 2018
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Sequential attend, infer, repeat: Generative modelling of moving objects
Adam R Kosiorek, Hyunjik Kim, Ingmar Posner, and Yee Whye Teh · 2018
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3d-rcnn: Instance-level 3d object reconstruction via render-and-compare
Abhijit Kundu, Yin Li, and James M Rehg · 2018
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Which training methods for gans do actually converge?
Lars Mescheder, Andreas Geiger, and Sebastian Nowozin · 2018
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Neural sparse voxel fields
Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt · 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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Nerf: Representing scenes as neural radiance fields for view synthesis
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng · 2020
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Giraffe: Representing scenes as compositional generative neural feature fields
Michael Niemeyer and Andreas Geiger · 2020
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Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision
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Shunyu Yao, Tzu Ming Harry Hsu, Jun-Yan Zhu, Jiajun Wu, Antonio Torralba, William T Freeman, and Joshua B Tenenbaum · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
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Monet: Unsupervised scene decomposition and representation
Christopher P Burgess, Loic Matthey, Nicholas Watters, Rishabh Kabra, Irina Higgins, Matt Botvinick, and Alexander Lerchner · 2019
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Spatially invariant unsupervised object detection with convolutional neural networks
Eric Crawford and Joelle Pineau · 2019
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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 · 2019
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Multi-object representation learning with iterative variational inference
Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Christopher Burgess, Daniel Zoran, Loic Matthey, Matthew Botvinick, and Alexander Lerchner · 2019
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Scalable object-oriented sequential generative models
Jindong Jiang, Sepehr Janghorbani, Gerard de Melo, and Sungjin Ahn · 2019
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Michael Niemeyer, Lars Mescheder, Michael Oechsle, and Andreas Geiger · 2020
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DeRF: Decomposed radiance fields
Daniel Rebain, Wei Jiang, Soroosh Yazdani, Ke Li, Kwang Moo Yi, and Andrea Tagliasacchi · 2020
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Graf: Generative radiance fields for 3d-aware image synthesis
Katja Schwarz, Yiyi Liao, Michael Niemeyer, and Andreas Geiger · 2020
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State of the art on neural rendering
Ayush Tewari, Ohad Fried, Justus Thies, Vincent Sitzmann, Stephen Lombardi, Kalyan Sunkavalli, Ricardo Martin-Brualla, Tomas Simon, Jason Saragih, Matthias Nießner, et al · 2020
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Toward unsupervised, multi-object discovery in large-scale image collections
Huy V Vo, Patrick Pérez, and Jean Ponce · 2020
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Pixelnerf: Neural radiance fields from one or few images
Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa · 2020
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Attention is not all you need: Pure attention loses rank doubly exponentially with depth
Yihe Dong, Jean-Baptiste Cordonnier, and Andreas Loukas · 2021
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Nerf-vae: A geometry aware 3d scene generative model
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Unsupervised layered image decomposition into object prototypes
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Donerf: Towards real-time rendering of neural radiance fields using depth oracle networks
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Campari: Camera-aware decomposed generative neural radiance fields
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Neural scene graphs for dynamic scenes
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Decomposing 3d scenes into objects via unsupervised volume segmentation
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