Anytime Recognition of Objects and Scenes
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Trevor Darrell | Mario Fritz | Sergey Karayev | Mario Fritz | Trevor Darrell | S. Karayev | Sergey Karayev
[1] M. L. Fisher,et al. An analysis of approximations for maximizing submodular set functions—I , 1978, Math. Program..
[2] David J. Field,et al. Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.
[3] Joshua B. Tenenbaum,et al. Separating Style and Content with Bilinear Models , 2000, Neural Computation.
[4] Trevor Hastie,et al. Imputing Missing Data for Gene Expression Arrays , 2001 .
[5] Paul A. Viola,et al. Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.
[6] S. Thorpe,et al. The Time Course of Visual Processing: From Early Perception to Decision-Making , 2001, Journal of Cognitive Neuroscience.
[7] Nicholas Roy,et al. Exponential Family PCA for Belief Compression in POMDPs , 2002, NIPS.
[8] Daniel Keren,et al. Painter identification using local features and naive Bayes , 2002, Object recognition supported by user interaction for service robots.
[9] Nello Cristianini,et al. Learning the Kernel Matrix with Semidefinite Programming , 2002, J. Mach. Learn. Res..
[10] Dieter Fox,et al. Reinforcement learning for sensing strategies , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).
[11] Antonio Torralba,et al. Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.
[12] David G. Lowe,et al. Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.
[13] Siwei Lyu,et al. A digital technique for art authentication , 2004, Proc. Natl. Acad. Sci. USA.
[14] Antonio Torralba,et al. Contextual Models for Object Detection Using Boosted Random Fields , 2004, NIPS.
[15] Andreas Krause,et al. Near-optimal Nonmyopic Value of Information in Graphical Models , 2005, UAI.
[16] Pierre Geurts,et al. Tree-Based Batch Mode Reinforcement Learning , 2005, J. Mach. Learn. Res..
[17] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[18] Jonathan Brandt,et al. Robust object detection via soft cascade , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[19] Xiaodong Fan. Efficient multiclass object detection by a hierarchy of classifiers , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[20] Bill Triggs,et al. Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[21] Cordelia Schmid,et al. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[22] James Ze Wang,et al. Studying Aesthetics in Photographic Images Using a Computational Approach , 2006, ECCV.
[23] Pietro Perona,et al. Graph-Based Visual Saliency , 2006, NIPS.
[24] Eli Shechtman,et al. Matching Local Self-Similarities across Images and Videos , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[25] Lawrence Carin,et al. Cost-sensitive feature acquisition and classification , 2007, Pattern Recognit..
[26] Thomas Hofmann,et al. Efficient Structure Learning of Markov Networks using L1-Regularization , 2007 .
[27] P. Perona,et al. What do we perceive in a glance of a real-world scene? , 2007, Journal of vision.
[28] Antonio Torralba,et al. Sharing Visual Features for Multiclass and Multiview Object Detection , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[29] Nando de Freitas,et al. Target-directed attention: Sequential decision-making for gaze planning , 2008, 2008 IEEE International Conference on Robotics and Automation.
[30] Christoph H. Lampert,et al. Beyond sliding windows: Object localization by efficient subwindow search , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[31] J. Hegdé. Time course of visual perception: Coarse-to-fine processing and beyond , 2008, Progress in Neurobiology.
[32] Alexei A. Efros,et al. An empirical study of context in object detection , 2009, CVPR.
[33] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[34] Geoffrey E. Hinton,et al. Factored conditional restricted Boltzmann Machines for modeling motion style , 2009, ICML '09.
[35] Charless C. Fowlkes,et al. Discriminative models for multi-class object layout , 2009, ICCV.
[36] Nicholas J. Butko,et al. Optimal scanning for faster object detection , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[37] Trevor Darrell,et al. Learning to Hash with Binary Reconstructive Embeddings , 2009, NIPS.
[38] Olivier R. Joubert,et al. The Time-Course of Visual Categorizations: You Spot the Animal Faster than the Bird , 2009, PloS one.
[39] Sebastian Nowozin,et al. On feature combination for multiclass object classification , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[40] Andrew Zisserman,et al. Multiple kernels for object detection , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[41] Yehuda Koren,et al. Matrix Factorization Techniques for Recommender Systems , 2009, Computer.
[42] Tsuhan Chen,et al. > Replace This Line with Your Paper Identification Number (double-click Here to Edit) < , 2022 .
[43] David A. McAllester,et al. Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[44] Serge J. Belongie,et al. Computer Vision and Image Understanding , 2022, SSRN Electronic Journal.
[45] Ashish Kapoor,et al. Visual recognition and detection under bounded computational resources , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[46] Yoram Singer,et al. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..
[47] David A. McAllester,et al. Cascade object detection with deformable part models , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[48] Andreas Krause,et al. Adaptive Submodularity: A New Approach to Active Learning and Stochastic Optimization , 2010, COLT 2010.
[49] Krista A. Ehinger,et al. SUN database: Large-scale scene recognition from abbey to zoo , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[50] Andreas Krause,et al. Near-Optimal Bayesian Active Learning with Noisy Observations , 2010, NIPS.
[51] Fei-Fei Li,et al. What Does Classifying More Than 10, 000 Image Categories Tell Us? , 2010, ECCV.
[52] Ian McGraw,et al. FastInf: An Efficient Approximate Inference Library , 2010, J. Mach. Learn. Res..
[53] Lior Shamir,et al. Impressionism, expressionism, surrealism: Automated recognition of painters and schools of art , 2010, TAP.
[54] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[55] Larry S. Davis,et al. Birdlets: Subordinate categorization using volumetric primitives and pose-normalized appearance , 2011, 2011 International Conference on Computer Vision.
[56] Kristen Grauman,et al. Large-Scale Live Active Learning: Training Object Detectors with Crawled Data and Crowds , 2011, CVPR 2011.
[57] Daphne Koller,et al. Active Classification based on Value of Classifier , 2011, NIPS.
[58] Vicente Ordonez,et al. High level describable attributes for predicting aesthetics and interestingness , 2011, CVPR 2011.
[59] Alexander C. Berg,et al. Fast and Balanced: Efficient Label Tree Learning for Large Scale Object Recognition , 2011, NIPS.
[60] Jianxiong Xiao,et al. What makes an image memorable? , 2011, CVPR 2011.
[61] Trevor Darrell,et al. Timely Object Recognition , 2012, NIPS.
[62] Jason Eisner,et al. Cost-sensitive Dynamic Feature Selection , 2012 .
[63] Kilian Q. Weinberger,et al. Classifier Cascade for Minimizing Feature Evaluation Cost , 2012, AISTATS.
[64] Lorenzo Torresani,et al. Meta-class features for large-scale object categorization on a budget , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[65] Kilian Q. Weinberger,et al. The Greedy Miser: Learning under Test-time Budgets , 2012, ICML.
[66] Venkatesh Saligrama,et al. Multi-Stage Classier Design , 2012 .
[67] J. Andrew Bagnell,et al. SpeedBoost: Anytime Prediction with Uniform Near-Optimality , 2012, AISTATS.
[68] Naila Murray,et al. AVA: A large-scale database for aesthetic visual analysis , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[69] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[70] Balázs Kégl,et al. Fast classification using sparse decision DAGs , 2012, ICML.
[71] Patrick Gallinari,et al. Sequential approaches for learning datum-wise sparse representations , 2012, Machine Learning.
[72] David Tolpin,et al. Selecting Computations: Theory and Applications , 2012, UAI.
[73] Yee Whye Teh,et al. Searching for objects driven by context , 2012, NIPS.
[74] Jonathan Krause,et al. Hedging your bets: Optimizing accuracy-specificity trade-offs in large scale visual recognition , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[75] Alexei A. Efros,et al. Dating Historical Color Images , 2012, ECCV.
[76] Matt J. Kusner,et al. Cost-Sensitive Tree of Classifiers , 2012, ICML.
[77] Koen E. A. van de Sande,et al. Selective Search for Object Recognition , 2013, International Journal of Computer Vision.
[78] Siddhartha S. Srinivasa,et al. Efficient touch based localization through submodularity , 2012, 2013 IEEE International Conference on Robotics and Automation.
[79] Alex Graves,et al. Generating Sequences With Recurrent Neural Networks , 2013, ArXiv.
[80] Florent Perronnin,et al. Learning beautiful (and ugly) attributes , 2013, BMVC.
[81] Venkatesh Saligrama,et al. Multi-stage classifier design , 2012, Machine Learning.
[82] Luc Van Gool,et al. The Interestingness of Images , 2013, 2013 IEEE International Conference on Computer Vision.
[83] Ben Taskar,et al. Dynamic Structured Model Selection , 2013, 2013 IEEE International Conference on Computer Vision.
[84] Rongrong Ji,et al. Large-scale visual sentiment ontology and detectors using adjective noun pairs , 2013, ACM Multimedia.
[85] Andreas Krause,et al. Active Detection via Adaptive Submodularity , 2014, ICML.
[86] Thomas Mensink,et al. The Rijksmuseum Challenge: Museum-Centered Visual Recognition , 2014, ICMR.
[87] John Langford,et al. A reliable effective terascale linear learning system , 2011, J. Mach. Learn. Res..
[88] Trevor Darrell,et al. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[89] R. Fergus,et al. OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.
[90] Dumitru Erhan,et al. Scalable Object Detection Using Deep Neural Networks , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[91] Trevor Darrell,et al. Part-Based R-CNNs for Fine-Grained Category Detection , 2014, ECCV.
[92] Matthieu Cord,et al. Sequentially Generated Instance-Dependent Image Representations for Classification , 2014, ICLR.
[93] Raffay Hamid,et al. What makes an image popular? , 2014, WWW.
[94] Trevor Darrell,et al. DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition , 2013, ICML.
[95] Trevor Darrell,et al. Recognizing Image Style , 2013, BMVC.
[96] Song-Chun Zhu,et al. Visual Persuasion: Inferring Communicative Intents of Images , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[97] Andrew Zisserman,et al. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps , 2013, ICLR.
[98] Jian Sun,et al. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.