| Articulated pose estimation with flexible mixtures-of-parts Y Yang, D Ramanan CVPR 2011, 1385-1392, 2011 | 1459 | 2011 |
| Deep captioning with multimodal recurrent neural networks (m-rnn) J Mao, W Xu, Y Yang, J Wang, A Yuille arXiv preprint arXiv:1412.6632, 2014 | 1357 | 2014 |
| Attention to scale: Scale-aware semantic image segmentation LC Chen, Y Yang, J Wang, W Xu, AL Yuille Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 1338 | 2016 |
| Cnn-rnn: A unified framework for multi-label image classification J Wang, Y Yang, J Mao, Z Huang, C Huang, W Xu Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 1209 | 2016 |
| Articulated human detection with flexible mixtures of parts Y Yang, D Ramanan IEEE transactions on pattern analysis and machine intelligence 35 (12), 2878 …, 2012 | 941 | 2012 |
| Learning from Massive Noisy Labeled Data for Image Classification T Xiao, T Xia, Y Yang, C Huang, X Wang IEEE Conference on Computer Vision and Pattern Recognition, 2015 | 935 | 2015 |
| Video Paragraph Captioning Using Hierarchical Recurrent Neural Networks H Yu, J Wang, Z Huang, Y Yang, X Wei Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016 | 631 | 2016 |
| Densebox: Unifying landmark localization with end to end object detection L Huang, Y Yang, Y Deng, Y Yu arXiv preprint arXiv:1509.04874, 2015 | 466 | 2015 |
| Look and think twice: Capturing top-down visual attention with feedback convolutional neural networks C Cao, X Liu, Y Yang, Y Yu, J Wang, Z Wang, Y Huang, L Wang, C Huang, ... Proceedings of the IEEE international conference on computer vision, 2956-2964, 2015 | 434 | 2015 |
| Explain images with multimodal recurrent neural networks J Mao, W Xu, Y Yang, J Wang, AL Yuille arXiv preprint arXiv:1410.1090, 2014 | 430 | 2014 |
| Occlusion aware unsupervised learning of optical flow Y Wang, Y Yang, Z Yang, L Zhao, P Wang, W Xu Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 280 | 2018 |
| Depth-based hand pose estimation: methods, data, and challenges J Supancic, G Rogez, Y Yang, J Shotton, D Ramanan arXiv preprint arXiv:1504.06378, 2015 | 278* | 2015 |
| Sequential convex approximations to joint chance constrained programs: A Monte Carlo approach LJ Hong, Y Yang, L Zhang Operations Research 59 (3), 617-630, 2011 | 194 | 2011 |
| Layered object models for image segmentation Y Yang, S Hallman, D Ramanan, CC Fowlkes IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (9), 1731-1743, 2011 | 182 | 2011 |
| Learning like a child: Fast novel visual concept learning from sentence descriptions of images J Mao, X Wei, Y Yang, J Wang, Z Huang, AL Yuille Proceedings of the IEEE International Conference on Computer Vision, 2015 | 175 | 2015 |
| Depth-based hand pose estimation: data, methods, and challenges JS Supancic, G Rogez, Y Yang, J Shotton, D Ramanan Proceedings of the IEEE international conference on computer vision, 1868-1876, 2015 | 168 | 2015 |
| Depth-based hand pose estimation: data, methods, and challenges J Supancic, G Rogez, Y Yang, J Shotton, D Ramanan Proceedings of the IEEE International Conference on Computer Vision, 1868-1876, 2015 | 168 | 2015 |
| Layered object detection for multi-class segmentation Y Yang, S Hallman, D Ramanan, C Fowlkes 2010 IEEE Computer Society Conference on Computer Vision and Pattern …, 2010 | 131 | 2010 |
| Fully convolutional attention networks for fine-grained recognition X Liu, T Xia, J Wang, Y Yang, F Zhou, Y Lin arXiv preprint arXiv:1603.06765, 2016 | 130 | 2016 |
| Unos: Unified unsupervised optical-flow and stereo-depth estimation by watching videos Y Wang, P Wang, Z Yang, C Luo, Y Yang, W Xu Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 125 | 2019 |
Deva RamananProfessor, Robotics Institute, Carnegie Mellon UniversityEmail verificata su cs.cmu.edu
