Attend, infer, repeat: Fast scene understanding with generative models

Advances in Neural Information Processing Systems

Volumn , Issue , 2016, Pages 3233-3241

  Ali Eslami, S M ^a   Heess, Nicolas ^a   Weber, Theophane ^a   Tassa, Yuval ^a   Szepesvari, David ^a   Kavukcuoglu, Koray ^a   Hinton, Geoffrey E ^a  

^a DEEPMIND   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

3-D IMAGE; ACCURATE INFERENCE; AUTO ENCODERS; GENERATIVE MODEL; MULTIPLE OBJECTS; PROBABILISTIC INFERENCE; SCENE UNDERSTANDING; STRUCTURED IMAGES;

RECURRENT NEURAL NETWORKS;

EID: 85018882970     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (28)

1. Jimmy Ba, Volodymyr Mnih, and Koray Kavukcuoglu. Multiple Object Recognition with Visual Attention. In ICLR, 2015.
2. Alex Graves. Adaptive computation time for recurrent neural networks. abs/1603.08983, 2016.
3. Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Rezende, and Daan Wierstra. DRAW: A Recurrent Neural Network For Image Generation. In ICML, 2015.
4. Ulf Grenander. Pattern Synthesis: Lectures in Pattern Theory. 1976.
5. Geoffrey E. Hinton, Peter Dayan, Brendan J. Frey, and Randford M. Neal. The "wake-sleep" algorithm for unsupervised neural networks. Science, 268(5214), 1995.
6. Max Jaderberg, Karen Simonyan, Andrew Zisserman, and Koray Kavukcuoglu. Spatial Transformer Networks. 2015.
7. Varun Jampani, Sebastian Nowozin, Matthew Loper, and Peter V. Gehler. The Informed Sampler: A Discriminative Approach to Bayesian Inference in Generative Computer Vision Models. CVIU, 2015.
8. Alex Kendall, Matthew Grimes, and Roberto Cipolla. PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization. In ICCV, 2015.
9. Diederik P Kingma and Max Welling. Auto-encoding variational Bayes. arXiv preprint arXiv: 1312.6114, 2013.
10. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. ImageNet Classification with Deep Convolutional Neural Networks. In NIPS 25, 2012.
11. Tejas D. Kulkarni, Pushmeet Kohli, Joshua B. Tenenbaum, and Vikash K. Mansinghka. Picture: A probabilistic programming language for scene perception. In CVPR, 2015.
12. Tejas D Kulkarni, William F. Whitney, Pushmeet Kohli, and Josh Tenenbaum. Deep Convolutional Inverse Graphics Network. In NIPS 28. 2015.
13. Brenden M. Lake, Ruslan Salakhutdinov, and Joshua B. Tenenbaum. Human-level concept learning through probabilistic program induction. Science, 350(6266), 2015.
14. Victor Lempitsky and Andrew Zisserman. Learning To Count Objects in Images. In NIPS 23. 2010.
15. Matthew M. Loper and Michael J. Black. OpenDR: An Approximate Differentiable Renderer. In ECCV, Volume 8695, 2014.
16. Vikash Mansinghka, Tejas Kulkarni, Yura Perov, and Josh Tenenbaum. Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs. In NIPS 26. 2013.
17. Brian Milch, Bhaskara Marthi, Stuart Russell, David Sontag, Daniel L. Ong, and Andrey Kolobov. BLOG: Probabilistic Models with Unknown Objects. In International Joint Conference on Artificial Intelligence, pages 1352-1359, 2005.
18. Andriy Mnih and Karol Gregor. Neural Variational Inference and Learning. In ICML, 2014.
19. Volodymyr Mnih, Nicolas Heess, Alex Graves, and Koray Kavukcuoglu. Recurrent Models of Visual Attention. In NIPS 27, 2014.
20. Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis. Human-level control through deep reinforcement learning. Nature, 518, 2015.
21. Danilo J. Rezende, Shakir Mohamed, and Daan Wierstra. Stochastic Backpropagation and Approximate Inference in Deep Generative Models. In ICML, 2014.
22. Ruslan Salakhutdinov and Geoffrey Hinton. Deep Boltzmann Machines. In AISTATS, 2009.
23. John Schulman, Nicolas Heess, Theophane Weber, and Pieter Abbeel. Gradient Estimation Using Stochastic Computation Graphs. In NIPS 28. 2015.
24. Yichuan Tang, Ruslan Salakhutdinov, and Geoffrey Hinton. Tensor Analyzers. In ICML, 2013.
25. Yichuan Tang, Nitish Srivastava, and Ruslan Salakhutdinov. Learning Generative Models With Visual Attention. In NIPS 27, 2014.
26. Emanuel Todorov, Tom Erez, and Yuval Tassa. MuJoCo: A physics engine for model-based control. In ICIRS, 2012.
27. Jianming Zhang, Shuga Ma, Mehrnoosh Sameki, Stan Sclaroff, Margrit Betke, Zhe Lin, Xiaohui Shen, Brian Price, and Radomír Měch. Salient Object Subitizing. In CVPR, 2015.
28. Song-Chun Zhu and David Mumford. A Stochastic Grammar of Images. Foundations and Trends in Computer Graphics and Vision, 2(4), 2006.