Fetching the paper…
Reading the bibliography…
Convolutional Neural Networks (CNNs) have proved exceptional at learning representations for visual object categorization.
Principles of object perception
Elizabeth S. Spelke · 1990
Earlier work this paper cites.
Normalized Cuts and Image Segmentation
Jianbo Shi and Jitendra Malik · 2000
Earlier work this paper cites.
Efficient Graph-Based Image Segmentation
Pedro F. Felzenszwalb and Daniel P. Huttenlocher · 2004
Earlier work this paper cites.
Discrete exterior calculus, 2005
Mathieu Desbrun, Anil N. Hirani, Melvin Leok, and Jerrold E. Marsden · 2005
Earlier work this paper cites.
Contour detection and hierarchical image segmentation
Pablo Arbelaez, Michael Maire, Charless Fowlkes, and Jitendra Malik · 2010
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Video segmentation by non-local consensus voting
Alon Faktor and Michal Irani · 2014
Earlier work this paper cites.
Learning visual groups from co-occurrences in space and time
Phillip Isola, Daniel Zoran, Dilip Krishnan, and Edward H Adelson · 2015
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng · 2016
Earlier work this paper cites.
Attend, Infer, Repeat: Fast Scene Understanding with Generative Models
S. M. Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, Koray Kavukcuoglu, and Geoffrey E. Hinton · 2016
Earlier work this paper cites.
Improved Variational Inference with Inverse Autoregressive Flow
Diederik P. Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
Earlier work this paper cites.
Joint 2d-3d-semantic data for indoor scene understanding
Iro Armeni, Sasha Sax, Amir R Zamir, and Silvio Savarese · 2017
Earlier work this paper cites.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille · 2017
Earlier work this paper cites.
A Generative Vision Model That Trains with High Data Efficiency and Breaks Text-Based Captchas
Dileep George, Wolfgang Lehrach, Ken Kansky, Miguel Lázaro-Gredilla, Christopher Laan, Bhaskara Marthi, Xinghua Lou, Zhaoshi Meng, Yi Liu, Huayan Wang, Alex Lavin, and D. Scott Phoenix · 2017
Earlier work this paper cites.
Neural Expectation Maximization
Klaus Greff, Sjoerd van Steenkiste, and Jürgen Schmidhuber · 2017
Earlier work this paper cites.
beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher P. Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
Earlier work this paper cites.
Visual genome: Connecting language and vision using crowdsourced dense image annotations
Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma, et al · 2017
Cited alongside, same era.
Learning Features by Watching Objects Move
Deepak Pathak, Ross Girshick, Piotr Dollár, Trevor Darrell, and Bharath Hariharan · 2017
Cited alongside, same era.
Label propagation for clustering
Lovro Subelj · 2017
Cited alongside, same era.
Neural Scene Representation and Rendering
S. M. 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
Cited alongside, same era.
Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
Cited alongside, same era.
Cvxnets: Learnable convex decomposition
Boyang Deng, Kyle Genova, Soroosh Yazdani, Sofien Bouaziz, Geoffrey E. Hinton, and Andrea Tagliasacchi · 2019
Later among the works it cites.
Mesh r-cnn
Georgia Gkioxari, Jitendra Malik, and Justin Johnson · 2019
Later among the works it cites.
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
Later among the works it cites.
Evidence That Recurrent Circuits Are Critical to the Ventral Stream’s Execution of Core Object Recognition Behavior
Kohitij Kar, Jonas Kubilius, Kailyn Schmidt, Elias B. Issa, and James J. DiCarlo · 2019
Later among the works it cites.
Disentangling neural mechanisms for perceptual grouping
Junkyung Kim, Drew Linsley, Kalpit Thakkar, and Thomas Serre · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Heinrich Jiang, Jennifer Jang, and Samory Kpotufe · 2018
Cited alongside, same era.
Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects
Adam R. Kosiorek, Hyunjik Kim, Ingmar Posner, and Yee Whye Teh · 2018
Cited alongside, same era.
Learning particle dynamics for manipulating rigid bodies, deformable objects, and fluids
Yunzhu Li, Jiajun Wu, Russ Tedrake, Joshua B Tenenbaum, and Antonio Torralba · 2018
Cited alongside, same era.
Learning long-range spatial dependencies with horizontal gated recurrent units
Drew Linsley, Junkyung Kim, Vijay Veerabadran, Charles Windolf, and Thomas Serre · 2018
Cited alongside, same era.
Flexible Neural Representation for Physics Prediction
Damian Mrowca, Chengxu Zhuang, Elias Wang, Nick Haber, Li Fei-Fei, Joshua B. Tenenbaum, and Daniel L. K. Yamins · 2018
Cited alongside, same era.
Task-driven convolutional recurrent models of the visual system
Aran Nayebi, Daniel Bear, Jonas Kubilius, Kohitij Kar, Surya Ganguli, David Sussillo, James J DiCarlo, and Daniel L Yamins · 2018
Cited alongside, same era.
Learning instance segmentation by interaction
Deepak Pathak, Yide Shentu, Dian Chen, Pulkit Agrawal, Trevor Darrell, Sergey Levine, and Jitendra Malik · 2018
Cited alongside, same era.
Later among the works it cites.
3d-relnet: Joint object and relational network for 3d prediction
Nilesh Kulkarni, Ishan Misra, Shubham Tulsiani, and Abhinav Gupta · 2019
Later among the works it cites.
R-sqair: Relational sequential attend, infer, repeat
Aleksandar Stanic and Jürgen Schmidhuber · 2019
Later among the works it cites.
Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V Le · 2019
Later among the works it cites.
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 · 2019
Later among the works it cites.
Object discovery in videos as foreground motion clustering
Christopher Xie, Yu Xiang, Zaid Harchaoui, and Dieter Fox · 2019
Later among the works it cites.
Unsupervised Discovery of Parts, Structure, and Dynamics
Zhenjia Xu, Zhijian Liu, Chen Sun, Kevin Murphy, William T. Freeman, Joshua B. Tenenbaum, and Jiajun Wu · 2019
Later among the works it cites.
Genesis: Generative scene inference and sampling with object-centric latent representations
Martin Engelcke, Adam R Kosiorek, Oiwi Parker Jones, and Ingmar Posner · 2020
Closest in time.
Visual grounding of learned physical models
Yunzhu Li, Toru Lin, Kexin Yi, Daniel Bear, Daniel LK Yamins, Jiajun Wu, Joshua B Tenenbaum, and Antonio Torralba · 2020
Closest in time.
Stable and expressive recurrent vision models
Drew Linsley, Alekh Karkada Ashok, Lakshmi Narasimhan Govindarajan, Rex Liu, and Thomas Serre · 2020
Closest in time.
Pix2shape: Towards unsupervised learning of 3d scenes from images using a view-based representation
Sai Rajeswar, Fahim Mannan, Florian Golemo, Jérôme Parent-Lévesque, David Vazquez, Derek Nowrouzezahrai, and Aaron Courville · 2020
Closest in time.
Learning to simulate complex physics with graph networks
Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, and Peter W Battaglia · 2020
Closest in time.
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
Closest in time.
Going in circles is the way forward: the role of recurrence in visual inference
Ruben S van Bergen and Nikolaus Kriegeskorte · 2020
Closest in time.