Consensus message passing for layered graphical models

Journal of Machine Learning Research

Volumn 38, Issue , 2015, Pages 425-433

  Jampani, Varun ^a   Eslami, S M Ali ^b   Tarlow, Daniel ^b   Kohli, Pushmeet ^b   Winn, John ^b  

^a MAX PLANCK INSTITUTE FOR INTELLIGENT SYSTEMS   (Germany)

^b MICROSOFT RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER VISION; INFERENCE ENGINES;

ACCURATE INFERENCE; CONDITIONAL MODELS; EXPECTATION PROPAGATION; GENERATIVE MODEL; IMAGING PROCESS; INFERENCE ALGORITHM; LAYERED STRUCTURES; PROBABILISTIC REASONING;

MESSAGE PASSING;

EID: 84954316234     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (28)

1. Biswas, S., Aggarwal, G., and Chellappa, R. (2009). Robust estimation of albedo for illumination-invariant matching and shape recovery. Pattern Analysis and Machine Intelligence, 31(5):884-899.
2. Biswas, S. and Chellappa, R. (2010). Pose-robust albedo estimation from a single image. In Computer Vision and Pattern Recognition (CVPR), pages 2683-2690.
3. Criminisi, A. and Shotton, J. (2013). Decision Forests for Computer Vision and Medical Image Analysis. Springer Publishing Company, Incorporated.
4. Domke, J. (2011). Parameter learning with truncated message-passing. Computer Vision and Pattern Recognition (CVPR), pages 2937-2943.
5. Eslami, S. M. A., Tarlow, D., Kohli, P., and Winn, J. (2014). Just-In-Time Learning for Fast and Flexible Inference. In Neural Information Processing Systems (NIPS) 27, pages 154-162.
6. Georghiades, A., Belhumeur, P., and Kriegman, D. (2001). From few to many: Illumination cone models for face recognition under variable lighting and pose. Pattern Analysis and Machine Intelligence, 23(6).
7. Heess, N., Tarlow, D., and Winn, J. (2013). Learning to Pass Expectation Propagation Messages. In Neural Information Processing Systems (NIPS) 26, pages 3219-3227.
8. Hinton, G. E. (1990). Mapping part-whole hierarchies into connectionist networks. Artificial Intelligence, 46(1-2):47-75.
9. Hinton, G. E., Dayan, P., Frey, B. J., and Neal, R. M. (1995). The wake-sleep algorithm for unsupervised neural networks. Science, 268:1158-1161.
10. Jampani, V., Nowozin, S., Loper, M., and Gehler, P. V. (2014). The Informed Sampler: A Discriminative Approach to Bayesian Inference in Generative Computer Vision Models. arXivpreprint arXiv.1402.0859.
11. Kemelmacher-Shlizerman, I. and Basri, R. (2011). 3d face reconstruction from a single image using a single reference face shape. Pattern Analysis and Machine Intelligence, 33(2):394-405.
12. Kingma, D. P. and Welling, M. (2013). Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114.
13. Lee, K.-C., Ho, J., and Kriegman, D. (2005). Acquiring linear subspaces for face recognition under variable lighting. Pattern Analysis and Machine Intelligence, 27(5):684-698.
14. Minka, T. (2001). Expectation Propagation for Approximate Bayesian Inference. PhD thesis, Massachusetts Institute of Technology.
15. Minka, T., Winn, J., Guiver, J., and Knowles, D. (2012). Infer. NET 2.5. Microsoft Research Cambridge. Website URL: http://research.microsoft.com/infernet.
16. Munoz, D. (2013). Inference Machines. Parsing Scenes via Iterated Predictions. PhD thesis, The Robotics Institute, Carnegie Mellon University.
17. Munoz, D., Bagnell, J. A., and Hebert, M. (2010). Stacked Hierarchical Labeling. In European Conference on Computer Vision (ECCV), pages 57-70.
18. Queau, Y., Lauze, F., and Durou, J.-D. (2013). Solving the uncalibrated photometric stereo problem using total variation. Springer.
19. Rezende, D. J., Mohamed, S., and Wierstra, D. (2014). Stochastic backpropagation and approximate inference in deep generative models. In International Conference on Machine Learning (ICML), pages 1278-1286.
20. Ross, S., Munoz, D., Hebert, M., and Bagnell, J. A. (2011). Learning Message-Passing Inference Machines for Structured Prediction. In Computer Vision and Pattern Recognition (CVPR), pages 2737-2744.
21. Shapovalov, R., Vetrov, D., and Kohli, P. (2013). Spatial Inference Machines. In In Computer Vision and Pattern Recognition (CVPR), pages 2985-2992.
22. Stuhlmuller, A., Taylor, J., and Goodman, N. D. (2013). Learning Stochastic Inverses. In Neural Information Processing Systems (NIPS) 27, pages 3048-3056.
23. Tang, Y., Salakhutdinov, R., and Hinton, G. (2012). Deep lambertian networks. arXiv preprint arXiv.1206.6445.
24. Teets, D. and Whitehead, K. (1999). The Discovery of Ceres: How Gauss Became Famous. Mathematics Magazine, 72(2):83-93.
25. Tu, Z., Zhu, S.-C., and Shum, H.-Y. (2001). Image segmentation by data driven Markov chain Monte Carlo. In International Conference on Computer Vision (ICCV), pages 131-138.
26. Wang, Y., Zhang, L., Liu, Z., Hua, G., Wen, Z., Zhang, Z., and Samaras, D. (2009). Face relighting from a single image under arbitrary unknown lighting conditions. Pattern Analysis and Machine Intelligence, 31(11):1968-1984.
27. Winn, J. and Bishop, C. M. (2005). Variational Message Passing. Journal of Machine Learning Research, 6:661-694.
28. Zhang, L. and Samaras, D. (2006). Face recognition from a single training image under arbitrary unknown lighting using spherical harmonics. Pattern Analysis and Machine Intelligence, 28(3):351-363.