| Learn to grow: A continual structure learning framework for overcoming catastrophic forgetting X Li, Y Zhou, T Wu, R Socher, C Xiong International Conference on Machine Learning, 3925-3934, 2019 | 450 | 2019 |
| Auto-Context R-CNN B Li, T Wu, L Zhang, R Chu arXiv preprint arXiv:1807.02842, 2018 | 306* | 2018 |
| The thermal infrared visual object tracking VOT-TIR2015 challenge results M Felsberg, A Berg, G Hager, J Ahlberg, M Kristan, J Matas, A Leonardis, ... Proceedings of the IEEE International Conference on Computer Vision …, 2015 | 195 | 2015 |
| An integrated UGV-UAV system for construction site data collection K Asadi, AK Suresh, A Ender, S Gotad, S Maniyar, S Anand, M Noghabaei, ... Automation in Construction 112, 103068, 2020 | 193 | 2020 |
| A stochastic graph grammar for compositional object representation and recognition L Lin, T Wu, J Porway, Z Xu Pattern Recognition 42 (7), 1297-1307, 2009 | 179 | 2009 |
| Face detection with end-to-end integration of a convnet and a 3d model Y Li, B Sun, T Wu, Y Wang Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016 | 170 | 2016 |
| Image synthesis from reconfigurable layout and style W Sun, T Wu Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 155 | 2019 |
| Learning And-Or Model to Represent Context and Occlusion for Car Detection and Viewpoint Estimation T Wu, B Li, SC Zhu IEEE transactions on pattern analysis and machine intelligence 38 (9), 1829-1843, 2016 | 154* | 2016 |
| Learning attraction field representation for robust line segment detection N Xue, S Bai, F Wang, GS Xia, T Wu, L Zhang Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 142 | 2019 |
| Vision-based integrated mobile robotic system for real-time applications in construction K Asadi, H Ramshankar, H Pullagurla, A Bhandare, S Shanbhag, P Mehta, ... Automation in Construction 96, 470-482, 2018 | 127 | 2018 |
| Holistically-attracted wireframe parsing N Xue, T Wu, S Bai, F Wang, GS Xia, L Zhang, PHS Torr Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 126 | 2020 |
| Learning auxiliary monocular contexts helps monocular 3d object detection X Liu, N Xue, T Wu Proceedings of the AAAI Conference on Artificial Intelligence 36 (2), 1810-1818, 2022 | 114 | 2022 |
| Design sparse features for age estimation using hierarchical face model J Suo, T Wu, S Zhu, S Shan, X Chen, W Gao 2008 8th IEEE International Conference on Automatic Face & Gesture …, 2008 | 109 | 2008 |
| A numerical study of the bottom-up and top-down inference processes in and-or graphs T Wu, SC Zhu International journal of computer vision 93, 226-252, 2011 | 104 | 2011 |
| Online object tracking, learning and parsing with and-or graphs Y Lu, T Wu, S Chun Zhu Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2014 | 100 | 2014 |
| Deep learning for remote sensing image understanding. L Zhang, GS Xia, T Wu, L Lin, XC Tai J. Sensors 2016 (2), 1-2, 2016 | 98 | 2016 |
| Attentive Normalization X Li, W Sun, T Wu arXiv preprint arXiv:1908.01259, 2019 | 94* | 2019 |
| Learning layout and style reconfigurable gans for controllable image synthesis W Sun, T Wu IEEE transactions on pattern analysis and machine intelligence 44 (9), 5070-5087, 2021 | 86 | 2021 |
| Stochastic-sign SGD for federated learning with theoretical guarantees R Jin, Y Huang, X He, H Dai, T Wu arXiv preprint arXiv:2002.10940, 2020 | 85 | 2020 |
| Discriminatively trained and-or tree models for object detection X Song, T Wu, Y Jia, SC Zhu Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2013 | 78 | 2013 |
song-chun zhuProfessor of Statistics and Computer Science, UCLAVerified email at stat.ucla.edu