| Detail-revealing deep video super-resolution X Tao, H Gao, R Liao, J Wang, J Jia Proceedings of the IEEE International Conference on Computer Vision, 4472-4480, 2017 | 175 | 2017 |
| 3d graph neural networks for rgbd semantic segmentation X Qi, R Liao, J Jia, S Fidler, R Urtasun Proceedings of the IEEE International Conference on Computer Vision, 5199-5208, 2017 | 152 | 2017 |
| Video super-resolution via deep draft-ensemble learning R Liao, X Tao, R Li, Z Ma, J Jia Proceedings of the IEEE International Conference on Computer Vision, 531-539, 2015 | 144 | 2015 |
| Deep edge-aware filters L Xu, J Ren, Q Yan, R Liao, J Jia International Conference on Machine Learning, 1669-1678, 2015 | 137 | 2015 |
| Learning Important Spatial Pooling Regions for Scene Classification D Lin, C Lu, R Liao, J Jia IEEE Computer Vision and Pattern Recognition, 2014 | 109 | 2014 |
| Geonet: Geometric neural network for joint depth and surface normal estimation X Qi, R Liao, Z Liu, R Urtasun, J Jia Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 105 | 2018 |
| Handling motion blur in multi-frame super-resolution Z Ma, R Liao, X Tao, L Xu, J Jia, E Wu Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2015 | 99 | 2015 |
| Upsnet: A unified panoptic segmentation network Y Xiong, R Liao, H Zhao, R Hu, M Bai, E Yumer, R Urtasun Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019 | 79 | 2019 |
| Learning to generate images with perceptual similarity metrics J Snell, K Ridgeway, R Liao, B Roads, MC Mozer, RS Zemel IEEE International Conference on Image Processing, 2017 | 71* | 2017 |
| Nervenet: Learning structured policy with graph neural networks T Wang, R Liao, J Ba, S Fidler International Conference on Learning Representations, 2018 | 60 | 2018 |
| Situation recognition with graph neural networks R Li, M Tapaswi, R Liao, J Jia, R Urtasun, S Fidler Proceedings of the IEEE International Conference on Computer Vision, 4173-4182, 2017 | 56 | 2017 |
| Learning deep parsimonious representations R Liao, A Schwing, R Zemel, R Urtasun Advances in Neural Information Processing Systems, 5076-5084, 2016 | 54 | 2016 |
| Lanczosnet: Multi-scale deep graph convolutional networks R Liao, Z Zhao, R Urtasun, RS Zemel International Conference on Learning Representations, 2019 | 52 | 2019 |
| Learning deep structured active contours end-to-end D Marcos, D Tuia, B Kellenberger, L Zhang, M Bai, R Liao, R Urtasun Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 50 | 2018 |
| Normalizing the normalizers: Comparing and extending network normalization schemes M Ren, R Liao, R Urtasun, FH Sinz, RS Zemel International Conference on Learning Representations, 2016 | 50 | 2016 |
| Inference in probabilistic graphical models by graph neural networks KJ Yoon, R Liao, Y Xiong, L Zhang, E Fetaya, R Urtasun, R Zemel, ... 2019 53rd Asilomar Conference on Signals, Systems, and Computers, 868-875, 2019 | 33 | 2019 |
| Understanding short-horizon bias in stochastic meta-optimization Y Wu, M Ren, R Liao, R Grosse International Conference on Learning Representations, 2018 | 33 | 2018 |
| Classification of benign and malignant breast tumors in ultrasound images based on multiple sonographic and textural features R Liao, T Wan, Z Qin 2011 Third International Conference on Intelligent Human-Machine Systems and …, 2011 | 33 | 2011 |
| A novel serial crime prediction model based on Bayesian learning theory R Liao, X Wang, L Li, Z Qin 2010 International Conference on Machine Learning and Cybernetics 4, 1757-1762, 2010 | 31 | 2010 |
| Semantic segmentation with object clique potential X Qi, J Shi, S Liu, R Liao, J Jia Proceedings of the IEEE International Conference on Computer Vision, 2587-2595, 2015 | 27 | 2015 |
Raquel UrtasunAssociate Professor, University of Toronto在 cs.toronto.edu 的电子邮件经过验证
