暂无分享,去创建一个
[1] Rada Mihalcea,et al. Mining semantic affordances of visual object categories , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[2] Yiannis Aloimonos,et al. Affordance detection of tool parts from geometric features , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).
[3] Fahad Shahbaz Khan,et al. Color attributes for object detection , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[4] Noah Snavely,et al. Material recognition in the wild with the Materials in Context Database , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[5] Yun Jiang,et al. Hallucinated Humans as the Hidden Context for Labeling 3D Scenes , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[6] Hema Swetha Koppula,et al. Anticipating Human Activities Using Object Affordances for Reactive Robotic Response , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[7] Barbara Caputo,et al. Using Object Affordances to Improve Object Recognition , 2011, IEEE Transactions on Autonomous Mental Development.
[8] Luc Van Gool,et al. What makes a chair a chair? , 2011, CVPR 2011.
[9] Kristen Grauman,et al. Relative attributes , 2011, 2011 International Conference on Computer Vision.
[10] Joachim M. Buhmann,et al. Towards weakly supervised semantic segmentation by means of multiple instance and multitask learning , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[11] James Hays,et al. SUN attribute database: Discovering, annotating, and recognizing scene attributes , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[12] Li Fei-Fei,et al. Reasoning about Object Affordances in a Knowledge Base Representation , 2014, ECCV.
[13] Bernt Schiele,et al. Evaluation of output embeddings for fine-grained image classification , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[14] Jianxiong Xiao,et al. DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[15] Joachim M. Buhmann,et al. Weakly supervised semantic segmentation with a multi-image model , 2011, 2011 International Conference on Computer Vision.
[16] Ali Farhadi,et al. Describing objects by their attributes , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[17] Jitendra Malik,et al. Hypercolumns for object segmentation and fine-grained localization , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[18] Honglak Lee,et al. Deep learning for detecting robotic grasps , 2013, Int. J. Robotics Res..
[19] Hema Swetha Koppula,et al. Learning human activities and object affordances from RGB-D videos , 2012, Int. J. Robotics Res..
[20] Andrew Zisserman,et al. Learning Visual Attributes , 2007, NIPS.
[21] Hema Swetha Koppula,et al. Physically Grounded Spatio-temporal Object Affordances , 2014, ECCV.
[22] 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.
[23] Trevor Darrell,et al. Learning to Detect Visual Grasp Affordance , 2016, IEEE Transactions on Automation Science and Engineering.
[24] Iasonas Kokkinos,et al. Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs , 2014, ICLR.
[25] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[26] George Papandreou,et al. Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation , 2015, ArXiv.
[27] Deva Ramanan,et al. Predicting Functional Regions on Objects , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.
[28] James M. Rehg,et al. Affordance Prediction via Learned Object Attributes , 2011 .
[29] J. Andrew Bagnell,et al. Perceiving, learning, and exploiting object affordances for autonomous pile manipulation , 2013, Auton. Robots.
[30] Trevor Darrell,et al. Constrained Convolutional Neural Networks for Weakly Supervised Segmentation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[31] Song-Chun Zhu,et al. Understanding tools: Task-oriented object modeling, learning and recognition , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Joachim M. Buhmann,et al. Weakly supervised structured output learning for semantic segmentation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[33] Sheng Zeng,et al. Weakly supervised semantic segmentation for social images , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[34] Silvio Savarese,et al. Recognizing human actions by attributes , 2011, CVPR 2011.
[35] Danica Kragic,et al. Visual object-action recognition: Inferring object affordances from human demonstration , 2011, Comput. Vis. Image Underst..
[36] Jia Xu,et al. Tell Me What You See and I Will Show You Where It Is , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[37] 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.
[38] Gaurav S. Sukhatme,et al. Semantic labeling of 3D point clouds with object affordance for robot manipulation , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).