Dense Semantic Image Segmentation with Objects and Attributes
暂无分享,去创建一个
Carsten Rother | Vibhav Vineet | Jonathan Warrell | Philip H. S. Torr | Paul Sturgess | Ming-Ming Cheng | Shuai Zheng | C. Rother | Paul Sturgess | Ming-Ming Cheng | Shuai Zheng | Vibhav Vineet | J. Warrell
[1] Vibhav Vineet,et al. ImageSpirit: Verbal Guided Image Parsing , 2013, ACM Trans. Graph..
[2] Shi-Min Hu,et al. Global contrast based salient region detection , 2011, CVPR 2011.
[3] Andrew Blake,et al. "GrabCut" , 2004, ACM Trans. Graph..
[4] Andrew Zisserman,et al. Learning Visual Attributes , 2007, NIPS.
[5] Stephen Gould,et al. Decomposing a scene into geometric and semantically consistent regions , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[6] Ramin Zabih,et al. Factorial Markov Random Fields , 2002, ECCV.
[7] Ali Farhadi,et al. Attribute-centric recognition for cross-category generalization , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[8] Andrew McCallum,et al. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.
[9] Antonio Torralba,et al. Sharing features: efficient boosting procedures for multiclass object detection , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..
[10] Patrick Pérez,et al. Interactive Image Segmentation Using an Adaptive GMMRF Model , 2004, ECCV.
[11] Derek Hoiem,et al. Indoor Segmentation and Support Inference from RGBD Images , 2012, ECCV.
[12] Vibhav Vineet,et al. Efficient Salient Region Detection with Soft Image Abstraction , 2013, 2013 IEEE International Conference on Computer Vision.
[13] Philip H. S. Torr,et al. What, Where and How Many? Combining Object Detectors and CRFs , 2010, ECCV.
[14] Kristen Grauman,et al. Relative attributes , 2011, 2011 International Conference on Computer Vision.
[15] Philip H. S. Torr,et al. What , Where & How Many ? Combining Object Detectors and CRFs , 2010 .
[16] Vladlen Koltun,et al. Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials , 2011, NIPS.
[17] Vladlen Koltun,et al. Parameter Learning and Convergent Inference for Dense Random Fields , 2013, ICML.
[18] Yang Wang,et al. A Discriminative Latent Model of Object Classes and Attributes , 2010, ECCV.
[19] Koen E. A. van de Sande,et al. Selective Search for Object Recognition , 2013, International Journal of Computer Vision.
[20] Christoph H. Lampert,et al. Augmented Attribute Representations , 2012, ECCV.
[21] Antonio Criminisi,et al. TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context , 2007, International Journal of Computer Vision.
[22] Andrew McCallum,et al. Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data , 2004, J. Mach. Learn. Res..
[23] Vibhav Vineet,et al. Filter-Based Mean-Field Inference for Random Fields with Higher-Order Terms and Product Label-Spaces , 2012, International Journal of Computer Vision.
[24] Pushmeet Kohli,et al. Associative hierarchical CRFs for object class image segmentation , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[25] Pushmeet Kohli,et al. Robust Higher Order Potentials for Enforcing Label Consistency , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[26] David A. McAllester,et al. Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[27] James Hays,et al. SUN attribute database: Discovering, annotating, and recognizing scene attributes , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[28] Yang Yu,et al. Multi-label hypothesis reuse , 2012, KDD.
[29] Thomas Deselaers,et al. Measuring the Objectness of Image Windows , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[30] Luc Van Gool,et al. The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.
[31] Silvio Savarese,et al. Relating Things and Stuff by High-Order Potential Modeling , 2012, ECCV Workshops.
[32] Yoram Singer,et al. Improved Boosting Algorithms Using Confidence-rated Predictions , 1998, COLT' 98.
[33] Daphne Koller,et al. Learning Spatial Context: Using Stuff to Find Things , 2008, ECCV.
[34] Svetlana Lazebnik,et al. Understanding scenes on many levels , 2011, 2011 International Conference on Computer Vision.
[35] Christoph H. Lampert,et al. Learning to detect unseen object classes by between-class attribute transfer , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[36] Philip H. S. Torr,et al. BING: Binarized normed gradients for objectness estimation at 300fps , 2014, Computational Visual Media.
[37] Krista A. Ehinger,et al. Basic level scene understanding: categories, attributes and structures , 2013, Front. Psychol..
[38] Philip H. S. Torr,et al. Scalable Cascade Inference for Semantic Image Segmentation , 2012, BMVC.
[39] Ali Farhadi,et al. Describing objects by their attributes , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[40] Ali Farhadi,et al. Recognition using visual phrases , 2011, CVPR 2011.
[41] Nir Friedman,et al. Probabilistic Graphical Models - Principles and Techniques , 2009 .