Technology: object, scene and event
Object modeling and recognition
[1] R. Salakhutdinov, A. Torralba, J. Tenenbaum, “Learning to Share Visual Appearance for Multiclass Object Detection,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.
[2] B. Yao, A. Khosla, and L. Fei-Fei, “Combining Randomization and Discrimination for Fine-Grained Image Categorization," IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2011.
[3] D. Ramanan. “Part-based Models for Finding People and Estimating Their Pose.” In T. Moeslund, A. Hilton, and L. Sigal (Eds.), Visual Analysis of Humans. Springer, Oct 2011.
[4] C. Desai, D. Ramanan, C. Fowlkes. “Discriminative Models for Multi-Class Object Layout,” International Journal of Computer Vision (IJCV). Oct 2011.
[5] Y. Yang, D. Ramanan. “Articulated Pose Estimation using Flexible Mixtures of Parts,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado, 2011. http://phoenix.ics.uci.edu/software/pose/
[6] D. Park, D. Ramanan. “N-Best Maximal Decoders for Part Models,” International Conference on Computer Vision (ICCV), Barcelona, Spain, November 2011.
[7] P. Dollar, S. Belongie and P. Perona, “The Fastest Pedestrian Detector in the West,” British Machine Vision Conference, 2010.
[8] M. Spain and P. Perona, “Measuring and predicting object importance,” International Journal of Computer Vision, vol. 91, no. 1,59-76, 2011.
[9] Z. Si and S.C. Zhu, “Learning Hybrid image Template (HiT) by Information Projection,” IEEE Trans. on Pattern Analysis and Machine Intelligence, (accepted, to appear), 2011.
[10] Z.Z. Si and S.C. Zhu, “Unsupervised Learning of Stochastic And-Or Templates for Object modeling,” Int’l Workshop on Stochastic Image Grammar, in junction with ICCV, Spain, 2011.
[11] B. Hariharan, P. Arbelaez, L. Bourdev, S. Maji, and J. Malik, “Semantic Contours from Inverse Detectors,” International Conference on Computer Vision (ICCV), 2011.
[12] L. Bourdev, and Subhransu Maji and Jitendra Malik", “Describing People: Poselet-Based Attribute Classification,” International Conference on Computer Vision (ICCV), 2011. http://www.eecs.berkeley.edu/~lbourdev/poselets"
[13] B. Rothrock and S.C. Zhu, “Human Parsing using Stochastic And-Or grammar and Rich Appearance,” Int’l Workshop on Stochastic Image Grammar, in junction with ICCV, Spain, 2011
Scene categorization and recognition
[14] L.-J. Li, H. Su, E.P. Xing and L. Fei-Fei, “Object Bank: A High-Level Image Representation for Scene Classification and Semantic Feature Sparsification,” Proceedings of the Neural Information Processing Systems (NIPS). 2010. http://vision.stanford.edu/projects/objectbank/index.html
Action and event understanding
[15] B. Yao, A. Khosla, and L. Fei-Fei, “Classifying Actions and Measuring Action Similarity by Modeling the Mutual Context of Objects and Human Poses,” International Conference on Machine Learning (ICML). 2011.
[16] B. Yao, X. Jiang, A. Khosla, A.L. Lin, L.J. Guibas, and L. Fei-Fei, “Human Action Recognition by Learning Bases of Action Attributes and Parts,” International Conference on Computer Vision (ICCV). 2011.
[17] M. Pei, Y. Jia, and S.-C. Zhu, “Parsing Video Events with Goal inference and Intent Prediction,” Int’l Conf. on Computer Vision (ICCV), Barcelona, Spain, 2011.
Synergy between objects, scenes, and events
[18] Z. Si, M. Pei, Z.Y. Yao, and S.-C. Zhu, “Unsupervised Learning of Event And-Or Grammar and Semantics from Video,” Int’l Conf. on Computer Vision (ICCV), Barcelona, Spain, 2011.
Theory: representation, learning and inference
Knowledge representation, cognitive modeling, reasoning and generalization
[19] W.Z. Hu, Y.N. Wu and S.-C. Zhu, “Image Representation by Active Curves,” Int’l Conf. on Computer Vision (ICCV), Barcelona, Spain, 2011.
[20] T. Gao and D. Koller. “Multiclass Boosting with Hinge Loss based on Output Coding,” International Conference on Machine Learning (ICML), 2011.
[21] T. Gao and D. Koller. “Discriminative Learning of Relaxed Hierarchy for Large-scale Visual Recognition,” IEEE International Conference on Computer Vision (ICCV), 2011.
[22] E. Bareinboim, C. Brito, and J. Pearl “Local Characterizations of Causal Bayesian Networks,” In M. Croitoru, O. Corby, J. Howse, S. Rudolph, and N. Wilson (Eds.), Proceedings of the Second International IJCAI Workshop on Graph Structures for Knowledge Representation and Reasoning (GKR 2011), 6-11, 2011.
[23] J. Pearl “Principal Stratification – A goal or a tool?,” The International Journal of Biostatistics Vol. 7: Iss. 1, 2011.
[24] P. Isola, J. Xiao, A. Torralba, A. Oliva, “What makes an image memorable?” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.
[25] E. Bareinboim and J. Pearl “Controlling Selection Bias in Causal Inference,” UCLA Cognitive Systems Laboratory, Technical Report (R-381), June 2011. Submitted
[26] J. Pearl “Comments and Controversies: Graphical models, potential outcomes and causal inference: Comment on Lindquist and Sobel,” Forthcoming, NeuroImage, 2011.
[27] J. Pearl “The Causal Mediation Formula – A Guide to the Assessment of Pathways and Mecha- nisms,” Forthcoming, Prevention Science, 2011.
[28] J. Pearl and E. Bareinboim “Transportability across studies: A formal approach,” Proceedings of AAAI, 2011.
[29] J. Pearl, “The algorithmization of counterfactuals,” Annals for Mathematics in AI, 2011.
[30] J. Pearl, “On the Consistency Rule in Causal Inference: An Axiom, Definition, Assumption, or a Theorem?,” Epidemiology, Vol. 21(6):872-875, 2010.
[31] J. Pearl, “On Measurement Bias in Causal Inference,” In P. Grunwald and P. Spirtes (eds.), Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence, 425– 432. AUAI, Corvallis, OR, 2010.
[32] J. Pearl, “On a Class of Bias-Amplifying Covariates that Endanger Effect Estimates,” In P. Grunwald and P. Spirtes, editors, Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence, 417–424. AUAI, Corvallis, OR, 2010.
[33] J. Pearl, “The Structural Theory of Causation,” In P. McKay Illari, F. Russo, and J. Williamson (Eds.), Causality in the Sciences, Clarendon Press, Chapter 33, pp. 697–727, Oxford, 2011.
[34] J. Pearl, “The Science and Ethics of Causal Modeling,” In Handbook of Ethics in Quantitative Methodology, A.T. Panter and Sonya Serba (Eds.), New York: Taylor and Francis Group, 383–414, 2011.
[35] J. Hamrick, P.W. Battaglia, and J.B. Tenenbaum, “Internal physics models guide probabilistic judgments about object dynamics,” Proceedings of the Thirty-Third Annual Conference of the Cognitive Science Society, 2011.
[36] C.L. Saker, R.R. Saxe, and J.B. Tenenbaum, “Bayesian Theory of Mind: Modeling Joint Belief-Desire Attribution,” Proceedings of the Thirty-Third Annual Conference of the Cognitive Science Society, 2011.
Theory for algorithms and inference
[37] J. Porway and S.C. Zhu, “C4: Computing Multiple Solutions in Graphical Models by Cluster Sampling,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.33, no.9, 1713-1727, 2011
[38] H. Pirsiavash, D. Ramanan, C. Fowlkes. “Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects,” Computer Vision and Pattern Recognition (CVPR), Colorado Springs, Colorado, June 2011. tracking_cvpr11_release_v1.0.tar.gzreleasev1.0.tar.gz
[39] S. Geman etc. “On the Invariance of Waiting Times Between Large Returns,” Journal of Financial Econometrics, (under review), 2011.
[40] S. Geman etc. “Conditional Modelling and the Jitter Method of Spike Re-Sampling,” Journal of Neurophysiology, (under review), 2011.
[41] G. Papandreou and A.L. Yuille, “Gaussian Sampling by Local Perturbation,” NIPS. 2010.
[42] G. Papandreou and A.L. Yuille, “Perturb-and-MAP Discrete-Valued Random Fields,” Int’l Conf. on Computer Vision, Barcelona, Spain, 2011.
[43] G. Papandreou and A.L. Yuille, “Efficient Variational Inference in Large-Scale Bayesian Compressed Sensing,” Proc. IEEE Workshop on Information Theory in Computer Vision and Pattern Recognition (in conjunction with ICCV-11), Barcelona, Spain, Nov. 2011.
[44] J. Pearl, “An Introduction to Causal Inference,” The International Journal of Biostatistics, Vol. 6 : Iss. 2,2011.
Dataset and Benchmarks
[45] A. Torralba, A. Efros, “Unbiased Look at Dataset Bias,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011. http://people.csail.mit.edu/torralba/research/bias/
[46] S. Branson, C. Wah, B. Babenko, F. Schroff, P. Welinder, P. Perona, and S. Belongie, “Visual Recognition with Humans in the Loop,” European Conference on Computer Vision (ECCV), Sept. 2010.
[47] P. Welinder, S. Branson, S. Belongie, and P. Perona, “The Multidimensional Wisdom of Crowds,” Conference on Neural Information Processing Systems (NIPS), Nov. 2010.
[48] P. Dollar, C. Wojek, B. Schiele, and P. Perona, “Pedestrian Detection: An Evaluation of the State of the Art,” IEEE Trans. on Pattern Analysis and Machine Intelligence, July, 304-311, 2011.
[49] E. Bart, M. Welling, and P. Perona, “Unsupervised organization of image collections: taxonomies and beyond,” IEEE Transactions on Pattern Analysis and Machine Intelligence, April, 2011.
Patents
[1] Method for Implementing a High-Level Image Representation for Image Analysis.
Li Fei-Fei, Li-Jia Li and Hao Su, US Patent No. 12,960,467
[2] System and Method for Representing and Encoding Images
Song-Chun Zhu, Cheng-en Guo, and Yingnian Wu US patent No. 7,567,715