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] O. Russakovsky, Y. Lin, K. Yu and L. Fei-Fei, “Object-centric spatial pooling for image classification,” European Conference on Computer Vision (ECCV), 2012.
[5] J. Deng, J. Krause, A. Berg, and L. Fei-Fei, “Hedging Your Bets: Optimizing Accuracy-Specificity Trade-offs in Large Scale Visual Recognition,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2012.
[6] B. Yao, G. Bradski, and L. Fei-Fei, “A Codebook-Free and Annotation-Free Approach for Fine-Grained Image Categorization,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2012.
[7] J. Deng, S. Satheesh, A. C. Berg, and L. Fei-Fei. “Fast and Balanced: Efficient Label Tree Learning for Large Scale Object Recognition,” Proceedings of the Neural Information Processing Systems (NIPS). 2011.
[8] B. Zhao, L. Fei-Fei and E. Xing. “Large-Scale Category Structure Aware Image Categorization,” Proc. of the Neural Information Processing Systems (NIPS). 2011.
[9] M. Andreetto L. Zelnik-Manor, and P. Perona, ”Unsupervised Learning of Categorical Segments in Image Collections,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, 9, 1842-1855, 2012.
[10] A.L. Yuille. “Towards a Theory of Compositional Learning and Encoding of Objects,” 1st IEEE Workshop in Information Theory in Computer Vision and Pattern Recognition. November, 2011.
[11] R. Mottaghi and A.L. Yuille. “A compositional approach to learning part-based models for single and multi-view object detection title,” 3dRR-11 workshop. November. 2011.
[12] R. Mottaghi, “Augmenting Deformable Part Models with Irregular-shaped Object Patches,” CVPR, 2012.
[13] C. Desai, D. Ramanan, C. Fowlkes. “Discriminative Models for Multi-Class Object Layout,” International Journal of Computer Vision (IJCV), Oct 2011.
[14] D. Park, D. Ramanan. “N-Best Maximal Decoders for Part Models” International Conference on Computer Vision (ICCV) Barcelona, Spain, November 2011.
[15] Z. Si and S.C. Zhu, “Learning Hybrid image Template (HiT) by Information Projection,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 34, no.7, pp1354-1367, 2012.
[16] L. Bourdev, and Subhransu Maji and Jitendra Malik", “Describing People: Poselet-Based Attribute Classification”, International Conference on Computer Vision (ICCV), 2011.
[17] 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.
[18] B. Hariharan, J. Malik, D. Ramanan. "Discriminative Decorrelation for Clustering and Classification," European Conference on Computer Vision (ECCV), 2012.
[19] X. Zhu, D. Ramanan. "Face Detection, Pose Estimation, and Landmark Localization in the Wild," Computer Vision and Pattern Recognition (CVPR), June 2012.
[20] H. Pirsiavash, D. Ramanan. "Steerable Part Models," Computer Vision and Pattern Recognition (CVPR), June 2012.
[21] M. Maire, S.X. Yu, and P. Perona, “Object detection and segmentation from joint embedding of parts and pixels,” IEEE International Conference on Computer Vision (ICCV), 2011.
[22] 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.
[23] B. Hariharan, P. Arbelaez, L. Bourdev, S. Maji, and J. Malik, “Semantic Contours from Inverse Detectors”, International Conference on Computer Vision (ICCV), 2011.
[24] C. Wah, S. Branson, P. Perona, and S. Belongie, “Multiclass recognition and part localization with humans in the loop,” IEEE International Conference on Computer Vision (ICCV), 2011.
[25] Y. Lu, B. Yao, Y. Wang and S.C. Zhu, “Reconfigurable Templates for Robust Vehicle Detection and Classification,” Workshop on Application of Computer Vision (WACV), Colorado, 2012.
[26] S. Branson, P. Perona, and S. Belongie, “Strong supervision from weak annotation: Interactive training of deformable part models,” IEEE International Conference on Computer Vision (ICCV), 2011.
[27] J. J. Lim, R. Salakhutdinov, A. Torralba, “Transfer Learning by Borrowing Examples for Multiclass Object Detection,” NIPS, Granada, Spain, Nov. 2011.
[28] R. Salakhutdinov, J. Tenenbaum and A. Torralba, “Learning to Learn with Compound Hierarchical-Deep Models,” NIPS, Granada, Spain, 2011.
[29] J. Xiao, B. Russell, and A. Torralba, “Localizing 3D cuboids in single-view images,” Submitted to NIPS 2012.
[30] J. Barron and J. Malik, ``Shape, Albedo, and Illumination from a Single Image of an Unknown Object’’, IEEE Conf. on CVPR, June, 2012.
[31] P. Arbelaez, B. Hariharan, C. Gu, S. Gupta, L. Bourdev and J. Malik, ``Semantic Segmentation using Regions and Parts’’, IEEE Conf. on CVPR, June, 2012.
[32] J. Barron and J. Malik, ``Color Constancy, Intrinsic Images and Shape Estimation’’, European Conf. on Computer vision (ECCV), 2012.
[33] B. Hariharan, J. Malik and D. Ramanan, ``Discriminative decorrelation for clustering and classification", European Conf. on Computer vision (ECCV), 2012.
[34] C. Gu, P. Arbelaez, Y. Lin, K. Yu, and J. Malik, “Multi-Component Models for Object Detection”, European Conf. on Computer vision (ECCV), 2012.
Scene categorization and recognition
[35] 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
[36] D. B. Walther, B. Chai, E. Caddigan, D. M. Beck and L. Fei-Fei, “Simple line drawings suffice for functional MRI decoding of natural scene categories,” Proc. Nat. Acad. of Sci (PNAS). vol. 108 (no. 23): pp9661-9666. 2011.
[37] G. Kim, L. Fei-Fei, and E. Xing, “Web Image Prediction Using Multivariate Point Processes,” The 18th ACM Conference on Knowledge Discovery and Data Mining (KDD). 2012.
[38] J. Xiao, K. A. Ehinger, A. Oliva and A. Torralba, “Recognizing Scene Viewpoint using Panoramic Place Representation,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
[39] J. Zhu, T.F. Wu, S.C. Zhu, X.K. Yang, W. Zhang , “Learning Reconfigurable Scene Representation by Tangram Model,” Workshop on Application of Computer Vision (WACV), Colorado, 2012.
Action and event understanding
[40] 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.
[41] B. Yao and L. Fei-Fei, “Action Recognition with Exemplar Based 2.5D Graph Matching,” European Conf. on Computer vision (ECCV), 2012.
[42] K. Tang, L. Fei-Fei, and D. Koller, “Learning Latent Temporal Structure for Complex Event Detection,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
[43] B. Yao and L. Fei-Fei, “Recognizing Human Actions in Still Images by Modeling the Mutual Context of Objects and Human Poses,” IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI). 34(9):1691-1703, September 2012.
[44] 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.
[45] F. Zabala, P. Polidoro, A. Robie, K. Branson, P. Perona, and M.H. Dickinson, “A Simple Strategy for Detecting Moving Objects during Locomotion Revealed by Animal-Robot Interactions,” Current Biology, 2012
[46] X.P. Burgos-Artizzu, P. Dollár, D. Lin, D.J. Anderson, and P. Perona, “Social behavior recognition in continuous video,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
[47] B. Yao, Z. Liu, and S.C. Zhu, “Animated Pose Templates for Modeling and Detecting Human Actions,” IEEE Trans on Pattern Analysis and Machine Intelligence (under 2nd review), 2012.
[48] 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.
[49] 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.
[50] Y. Yang, S. Baker, A. Kannan, D. Ramanan. "Recognizing Proxemics in Personal Photos," IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2012.
[51] C. Desai, D. Ramanan. "Detecting Actions, Poses, and Objects with Relational Phraselets," European Conference on Computer Vision (ECCV), 2012.
[52] J.J. Jara-Ettinger, C.L. Baker, and J.B. Tenenbaum, “Learning What is Where from Social Observations,” Proceedings of the 34th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society, 515-520, 2012.
[53] M.T. Pei, Z.Z. Si, B. Yao, and S.C. Zhu, “Video Event Parsing and Learning with Goal and Intent Prediction,” Computer Vision and Image Understanding, Under review, 2012.
Synergy between objects, scenes, and events
[54] 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), 2011.
[55] M. Choi, A. Torralba, and A. S. Willsky, “Context Models and Out-of-context Objects,” Pattern Recognition Letters, Volume 33, Issue 7, Pages 853-862, May 2012.
[56] C. Liu, J. Yuen and A. Torralba, “Nonparametric Scene Parsing via Label Transfer,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Vol 33, No. 12, 2011.
[57] M. Stark, J. Krause, B. Pepik, D. Meger, J J. Little, B. Schiele, and D. Koller, “Fine-Grained Categorization for 3D Scene Understanding,” British Machine Vision Conference, 2012.
Theory: representation, learning and inference
Knowledge representation, cognitive modeling, reasoning and generalization
[58] T. Gao and D. Koller. “Multiclass Boosting with Hinge Loss based on Output Coding”. International Conference on Machine Learning (ICML), 2011.
[59] 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.
[60] J. Pearl “Principal Stratification – A goal or a tool?,” The International Journal of Biostatistics Vol. 7: Iss. 1, 2011.
[61] P. Isola, J. Xiao, A. Torralba, A. Oliva, “What makes an image memorable?” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.
[62] J. Pearl, “The algorithmization of counterfactuals,” Annals for Mathematics in AI, 2011.
[63] J. Pearl, “On the Consistency Rule in Causal Inference: An Axiom, Definition, Assumption, or a Theorem?,” Epidemiology, Vol. 21(6):872-875, 2010.
[64] 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.
[65] 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.
[66] 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.
[67] 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.
[68] 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.
[69] W.Z. Hu, “Learning 3D Object Templates by Hierarchical Quantization of Geometry and Appearance Spaces,” Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Providence, RI, 2012.
[70] W.Z. Hu, Z. Si, and S.C. Zhu, “Structure v.s. Appearance and 3D v.s. 2D? A Numeric Answer,” Book chapter in Shape Perception in Human and Computer Vision: An Interdisciplinary Perspective edited by S. Dickinson and Z. Pizlo Cambridge University Press, 2012.
[71] J. Joo, S. Wang and S.C. Zhu, ‘Hierarchical Organization by And-Or Tree,” Book chapter in Handbook of Perceptual Organization, edited by.J. Wagemans, Springer, 2012
[72] T. Gao and D. Koller. “Discriminative Learning of Relaxed Hierarchy for Large-scale Visual Recognition”. IEEE International Conference on Computer Vision (ICCV), 2011.
[73] J. Pearl, “Invited Commentary: Understanding Bias Amplification,” American Journal of Epidemiology, 174(11):1223-1227,2011.
[74] J. Pearl “Comments and Controversies: Graphical models, potential outcomes and causal inference: Comment on Lindquist and Sobel,” NeuroImage, 58(3):770-771, October 2011.
[75] J. Pearl “The Causal Mediation Formula – A Guide to the Assessment of Pathways and Mechanisms,” Technical Report R-379, October 2011. Forthcoming, Prevention Science.
[76] S. Greenland and J. Pearl “Adjustments and their Consequences – Collapsibility Analysis using Graphical Models,” International Statistical Reviews, 79(3):401–426, 2011.
[77] J. Pearl, “Correlation and Causation – the logic of co-habitation,” European Journal of Personality, Special Issue, Accepted, 2012.
[78] M. Kuroki and J. Pearl, “Measurement Bias and Effect Restoration in Causal Inference,” UCLA Cognitive Systems Laboratory, Technical Report R-366, October, 2011.
[79] J. Pearl, “The Structural Theory of Causation,” In P. McKay Illari, F. Russo, and J. Williamson (Eds.), Causality in the Sciences, Chapter 33, Clarendon Press, Oxford, 697–727, 2011.
[80] E. Bareinboim and J. Pearl, “Controlling Selection Bias in Causal Inference,” In Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS), La Palma, Canary Islands, April 21-23, 2012.
[81] J. Pearl, “The Causal Foundations of Structural Equation Modeling,” UCLA Cognitive Systems Laboratory, Technical Report R-370, March 2012. Forthcoming, Chapter for R. H. Hoyle (Ed.), Handbook of Structural Equation Modeling. New York: Guilford Press.
[82] J. Pearl, “Interpretable conditions for identifying direct and indirect effects,” UCLA Cognitive Systems Laboratory, Technical Report R-389, March, 2012.
[83] E. Bareinboim and J. Pearl, “Transportability of Causal Effects: Completeness Results,” UCLA Cognitive Systems Laboratory, Technical Report R-390, January 2012. To appear in Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI), 2012.
[84] J. Pearl “Trygve Haavelmo and the Emergence of Causal Calculus,” UCLA Cognitive Systems Laboratory, Technical Report R-391, February 2012. Written for Econometric Theory, special issue on Haavelmo Centennial.
[85] K.A. Bollen and J. Pearl “Eight Myths about Causality and Structural Equation Models,” UCLA Cognitive Systems Laboratory, Technical Report (R-393), April 2012. Draft chapter for S. Morgan (Ed.), Handbook of Causal Analysis for Social Research, Springer 2012.
[86] J. Pearl “Correlation and Causation – the logic of co-habitation,” UCLA Cognitive Systems Laboratory, Technical Report (R-394), March 2012. Written for the European Journal of Personality, Special Issue.
[87] J. Pearl and E. Bareinboim “External Validity: From do-calculus to Transportability across Populations,” UCLA Cognitive Systems Laboratory, Technical Report R-400, May 2012. Submitted to Statistical Science.
[88] C.L. Saker, R.R. Saxe, and J.B. Tenenbaum, “Bayesian Theory of Mind: Modeling Joint Belief-Desire Attribution,” Proceedings of the 33rd Annual Conference of the Cognitive Science Society, 2011.
[89] P. Isola, D. Parikh, A. Torralba, and A. Oliva, “Understanding the intrinsic memorability of images,” NIPS, 2011, Granada, Spain.
[90] K. A. Ehinger, J. Xiao, A. Torralba, and A. Oliva, “Estimating scene typicality from human ratings and image features,” Proceedings of the 33rd Annual Conference of the Cognitive Science Society, Boston, MA: Cognitive Science Society 2011.
[91] P. Battaglia, T. Ullman, J. B. Tenenbaum et al., Workshop on Computational Models of Intuitive Physics, 34th Annual Conference of the Cognitive Science Society, 2012
[92] T. Gerstenberg, N.D. Goodman, D.A. Lagnado, and J.B. Tenenbaum, “Noisy Newtons: Unifying process and dependency accounts of causal attribution.” In N. Miyake, D. Peebles, & R. P. Cooper (Eds.), Proceedings of the 34th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society, 378-383, 2012.
[93] J. Hamrick, Physical reasoning in complex scenes is sensitive to mass. Unpublished M.Eng. thesis, Massachusetts Institute of Technology, Cambridge, MA, 2012.
[94] P.W. Battaglia, J. Hamrick, J. B. Tenenbaum, “Intuitive mechanics in visual reasoning about complex scenes with unknown forces,” Poster presented by Peter Battaglia at Annual Meeting of the Vision Sciences Society 2012, Naples, FL, May 2012.
[95] J. Hamrick, P.W. Battaglia, J.B. Tenenbaum, “Physics knowledge aids object perception in dynamic scenes,” Poster presented by Jessica Hamrick at Annual Meeting of the Vision Sciences Society 2012, Naples, FL, May 2012.
[96] T. Gao, M. Stark and D. Koller, “What Makes a Good Detector? — Structured Priors for Learning From Few Examples,” European Conf. on Computer Vision (ECCV), 2012
[97] T. Gao and D. Koller, “Active Classification Based on Value of Classifier,” NIPS, 2011
Theory for algorithms and inference
[98] 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. http://www.ics.uci.edu/~hpirsiav/papers/tracking_cvpr11_releasev1.0.tar.gz
[99] G. Papandreou and A.L. Yuille, “Gaussian Sampling by Local Perturbation,” NIPS. 2010.
[100] 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.
[101] J. Pearl, “An Introduction to Causal Inference,”_The International Journal of Biostatistics, Vol. 6 : Iss. 2_, 2011.
[102] M. R. Amer1, D. Xie, M. Zhao, S. Todorovic, and S.C. Zhu, “Cost-Sensitive Top-down/Bottom-up Inference for Multiscale Activity Recognition,” European Conf. on Computer Vision (ECCV), 2012.
[103] H. Su, A. Yu, and L. Fei-Fei. Efficient Euclidean Projections onto the Intersection of Norm Balls. International Conference on Machine Learning (ICML). 2012.
[104] 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.
[105] Lo-Bin Chang, Ya Jin, Wei Zhang, Eran Borenstein, and Stuart Geman, “Context, Computation, and Optimal ROC Performance in Hierarchical Models,” Int’l Journal of Computer Vision, 93(2), 117-140, 2011.
[106] A. Amarasingham, M.T. Harrison, N.G. Hatsopoulos, and S. Geman, “Conditional modeling and the jitter method of spike resampling,” Journal of Neurophysiology, 107(2), 517-531, 2012.
[107] Z. Si and S.C. Zhu, “Learning And-Or Templates for Object Modeling and Recognition,” IEEE Trans on Pattern Analysis and Machine Intelligence, under 2nd review. 2012.
[108] G. Papandreou and A.L. Yuille, “Perturb-and-MAP Discrete-Valued Random Fields,” Int’l Conf. on Computer Vision, Barcelona, Spain, 2011.
[109] D. Park and D. Ramanan, "N-Best Maximal Decoders for Part Models," Int’l Conference on Computer Vision (ICCV), Barcelona, Spain, November 2011.
[110] H. Wang and D. Koller, “Fast Exact MAP Inference by Passing Incomplete Messages,” under review for NIPS 2012.
[111] K. Miller, M. Pawan Kumar, B. Packer, D. Goodman and D. Koller, “Max-Margin Min-Entropy Models”, International Conference on Artificial Intelligence and Statistics (AISTATS), 2012
[112] M. Pawan Kumar, B. Packer and D. Koller, “Modeling Latent Variable Uncertainty for Loss-Based Learning,” Int’l Conf. Machine Learning, 2012.
Dataset and Benchmarks
[113] 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/
[114] P. Welinder, S. Branson, S. Belongie, and P. Perona, “The Multidimensional Wisdom of Crowds,” Conference on Neural Information Processing Systems (NIPS), Nov. 2010.
[115] ImageNet Large-Scale Visual Recognition Challenge (iLSVRC) 2012: http://www.image-net.org/challenges/LSVRC/2012/index
[116] 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.
[117] R. Gomes, P. Welinder, A. Krause, P. Perona, “Crowdclustering,” Neural Information Processing Systems (NIPS), 2011.
[118] C.Vondrick, D.Ramanan. "Video Annotation and Tracking with Active Learning," Neural Info. Proc. Systems (NIPS), Granada, Spain, Dec 2011.
[119] C. Vondrick, D. Patterson, D. Ramanan, "Efficiently Scaling Up Crowdsourced Video Annotation," International Journal of Computer Vision (IJCV). 2012.
[120] H. Pirsiavash, D. Ramanan. "Recognizing Activities of Daily Living in First-Person Camera Views," Computer Vision and Pattern Recognition (CVPR), Providence, RI, June, 2012.
[121] A. Khosla, T. Zhou, T. Malisiewicz, A. Efros, and A. Torralba, “Undoing the Damage of Dataset Bias,” European Conference on Computer Vision (ECCV), 2012.
[122] B. Kaneva, A. Torralba, W.T. Freeman, “Evaluation of Image Features Using a Photorealistic Virtual World,” Int’l Conf. on Computer vision, Barcelona, Spain, 2011.
[123] X. Zhu, C. Vondrick, D. Ramanan and C. Fowlkes. "Do We Need More Training Data or Better Models for Object Detection?" British Machine Vision Conference (BMVC), Surrey, UK, Sept. 2012.
[124] M. Aly, M. Munich, and P. Perona, “Distributed Kd-Trees for Retrieval from Very Large Image Collections,” British Machine Vision Conference (BMVC), Dundee, UK, 2011,
[125] E. Bart, M. Welling, P. Perona, “Unsupervised organization of image collections: taxonomies and beyond,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011.
[126] P. Dollár, C. Wojek, B. Schiele, and P. Perona, “Pedestrian detection: An evaluation of the state of the art,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011.
[127] C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie, The Caltech-UCSD Birds-200-2011 Dataset, Technical Report, California Institute of Technology.
Patents
Li Fei-Fei, Li-Jia Li and Hao Su, Method for Implementing a High-Level Image Representation for Image Analysis, US Patent No. 12/960,467
Song-Chun Zhu, Cheng-en Guo, and Yingnian Wu, System and Method for Representing and Encoding Images, US patent No. 7,567,715
Li Fei-Fei, Jia Deng, Jon Krause and Alex C. Berg, High Accuracy Object Recognition in Still Images. US Patent No. 61/659,940.
Olga Russakovsky, Yuanqing Lin, Kai Yu, Fei-Fei Li, Object centric spatial pooling for image classification. US Patent No. 61/561,846.