Generative modeling for continuous non-linearly embedded visual inference
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[1] David J. Fleet,et al. People tracking using hybrid Monte Carlo filtering , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.
[2] Michael J. Black,et al. Implicit Probabilistic Models of Human Motion for Synthesis and Tracking , 2002, ECCV.
[3] Michael Isard,et al. CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.
[4] Cristian Sminchisescu,et al. Kinematic jump processes for monocular 3D human tracking , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..
[5] M. Levas. OBBTree : A Hierarchical Structure for Rapid Interference Detection , .
[6] Mikhail Belkin,et al. Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering , 2001, NIPS.
[7] Kilian Q. Weinberger,et al. Unsupervised Learning of Image Manifolds by Semidefinite Programming , 2004, CVPR.
[8] Andrew Blake,et al. Probabilistic tracking in a metric space , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.
[9] Geoffrey E. Hinton,et al. A Mode-Hopping MCMC sampler , 2003 .
[10] Andrew Blake,et al. Articulated body motion capture by annealed particle filtering , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).
[11] Qiang Wang,et al. Learning object intrinsic structure for robust visual tracking , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..
[12] Cristian Sminchisescu,et al. Estimating Articulated Human Motion with Covariance Scaled Sampling , 2003, Int. J. Robotics Res..
[13] M. R. Osborne,et al. On the LASSO and its Dual , 2000 .
[14] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[15] C. Sminchisescu,et al. Variational mixture smoothing for non-linear dynamical systems , 2004, CVPR 2004.
[16] Stephen M. Omohundro,et al. Nonlinear manifold learning for visual speech recognition , 1995, Proceedings of IEEE International Conference on Computer Vision.
[17] D. Donoho,et al. Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[18] Michael I. Jordan,et al. On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.
[19] Dhairya Desai,et al. Visual Speech Recognition , 2020 .
[20] Joshua B. Tenenbaum,et al. Global Versus Local Methods in Nonlinear Dimensionality Reduction , 2002, NIPS.
[21] J. Tenenbaum,et al. A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.
[22] William T. Freeman,et al. Bayesian Reconstruction of 3D Human Motion from Single-Camera Video , 1999, NIPS.
[23] Yee Whye Teh,et al. Automatic Alignment of Local Representations , 2002, NIPS.
[24] Nicolas Le Roux,et al. Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering , 2003, NIPS.
[25] S T Roweis,et al. Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.