| Conditional random fields as recurrent neural networks S Zheng, S Jayasumana, B Romera-Paredes, V Vineet, Z Su, D Du, ... Proceedings of the IEEE International Conference on Computer Vision, 1529-1537, 2015 | 1787 | 2015 |
| Efficient salient region detection with soft image abstraction MM Cheng, J Warrell, WY Lin, S Zheng, V Vineet, N Crook Proceedings of the IEEE International Conference on Computer vision, 1529-1536, 2013 | 488 | 2013 |
| Robust view transformation model for gait recognition S Zheng, J Zhang, K Huang, R He, T Tan 2011 18th IEEE International Conference on Image Processing, 2073-2076, 2011 | 182 | 2011 |
| Higher order conditional random fields in deep neural networks A Arnab, S Jayasumana, S Zheng, PHS Torr European Conference on Computer Vision, 524-540, 2016 | 179* | 2016 |
| Dense Semantic Image Segmentation with Objects and Attributes S Zheng, MM Cheng, J Warrell, P Sturgess, V Vineet, C Rother, PHS Torr CVPR, 1-8, 2014 | 81 | 2014 |
| GP-GAN: Towards Realistic High-Resolution Image Blending H Wu, S Zheng, J Zhang, K Huang ACM MM 2019 / arXiv preprint arXiv:1703.07195, 2019 | 62 | 2019 |
| DenseCut: Densely Connected CRFs for Realtime GrabCut MM Cheng, VA Prisacariu, S Zheng, PHS Torr, C Rother Computer Graphics Forum 34, 2015 | 57 | 2015 |
| ImageSpirit: Verbal Guided Image Parsing MM Cheng, S Zheng, WY Lin, V Vineet, P Sturgess, N Crook, N Mitra, ... ACM Transactions on Graphics (TOG), 2014 | 55 | 2014 |
| Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation: Combining Probabilistic Graphical Models with Deep Learning for Structured Prediction A Arnab, S Zheng, S Jayasumana, B Romera-Paredes, M Larsson, ... IEEE Signal Processing Magazine 35 (1), 2018 | 41 | 2018 |
| Fast End-to-End Trainable Guided Filter H Wu, S Zheng, J Zhang, K Huang CVPR, 2018 | 36 | 2018 |
| A cascade fusion scheme for gait and cumulative foot pressure image recognition S Zheng, K Huang, T Tan, D Tao Pattern Recognition 45 (10), 3603-3610, 2012 | 33 | 2012 |
| Conditional Image-Text Embedding Networks BA Plummer, P Kordas, MH Kiapour, S Zheng, R Piramuthu, S Lazebnik ECCV, 2018 | 27 | 2018 |
| Fully-Trainable Deep Matching J Thewlis, S Zheng, P Torr, A Vedaldi BMVC, 2016 | 27 | 2016 |
| Approximate structured output learning for constrained local models with application to real-time facial feature detection and tracking on low-power devices S Zheng, P Sturgess, PHS Torr 2013 10th IEEE International Conference and Workshops on Automatic Face and …, 2013 | 24 | 2013 |
| ModaNet: A Large-Scale Street Fashion Dataset with Polygon Annotations S Zheng, F Yang, MH Kiapour, R Piramuthu ACM Multimedia, 2018 | 22 | 2018 |
| Evaluation framework on translation-invariant representation for cumulative foot pressure image S Zheng, K Huang, T Tan 2011 18th IEEE International Conference on Image Processing, 201-204, 2011 | 21 | 2011 |
| Object detection by context and boosted HOG-LBP Y Yu, J Zhang, Y Huang, S Zheng, W Ren, C Wang, K Huang, T Tan European Conference on Computer Vision (ECCV) VOC Workshop Talk, 104, 2010 | 21 | 2010 |
| Object Proposal Estimation in Depth Images Using Compact 3D Shape Manifolds S Zheng, VA Prisacariu, M Averkiou, MM Cheng, NJ Mitra, J Shotton, ... Pattern Recognition, 196-208, 2015 | 18 | 2015 |
| Efficient Likelihood Learning of a Generic CNN-CRF Model for Semantic Segmentation A Kirillov, D Schlesinger, S Zheng, B Savchynskyy, P Torr, C Rother ACCV, 2016 | 17* | 2016 |
| Joint training of generic cnn-crf models with stochastic optimization A Kirillov, D Schlesinger, S Zheng, B Savchynskyy, PHS Torr, C Rother Asian Conference on Computer Vision, 221-236, 2016 | 16 | 2016 |
Philip TorrProfessor, University of OxfordVerified email at eng.ox.ac.uk
