| Geodesic Flow Kernel for Unsupervised Domain Adaptation B Gong, Y Shi, F Sha, K Grauman CVPR, 2012 | 2154 | 2012 |
| Synthesized Classifiers for Zero-Shot Learning S Changpinyo, WL Chao, B Gong, F Sha arXiv preprint arXiv:1603.00550 (Accepted to CVPR 2016), 2016 | 732 | 2016 |
| Large-scale long-tailed recognition in an open world Z Liu, Z Miao, X Zhan, J Wang, B Gong, SX Yu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 447 | 2019 |
| An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild WL Chao, S Changpinyo, B Gong, F Sha arXiv preprint arXiv:1605.04253 (Accepted to ECCV 2016), 2016 | 445 | 2016 |
| Curriculum domain adaptation for semantic segmentation of urban scenes Y Zhang, P David, B Gong Proceedings of the IEEE international conference on computer vision, 2020-2030, 2017 | 414 | 2017 |
| Diverse sequential subset selection for supervised video summarization B Gong, WL Chao, K Grauman, F Sha Advances in neural information processing systems 27, 2014 | 413 | 2014 |
| Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation B Gong, K Grauman, F Sha International conference on machine learning, 222-230, 2013 | 409 | 2013 |
| Adversarial examples improve image recognition C Xie, M Tan, B Gong, J Wang, AL Yuille, QV Le Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 288 | 2020 |
| Improving the improved training of wasserstein gans: A consistency term and its dual effect X Wei, B Gong, Z Liu, W Lu, L Wang arXiv preprint arXiv:1803.01541, 2018 | 213 | 2018 |
| F. Sha. Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation B Gong, K Grauman ICML, 2013 | 195 | 2013 |
| Learning Attributes Equals Multi-Source Domain Generalization C Gan, T Yang, B Gong arXiv preprint arXiv:1605.00743 (Accepted to CVPR 2016), 2016 | 190 | 2016 |
| Spatiotemporal contrastive video representation learning R Qian, T Meng, B Gong, MH Yang, H Wang, S Belongie, Y Cui Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 179 | 2021 |
| End-to-end learning of motion representation for video understanding L Fan, W Huang, C Gan, S Ermon, B Gong, J Huang Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 179 | 2018 |
| Nattack: Learning the distributions of adversarial examples for an improved black-box attack on deep neural networks Y Li, L Li, L Wang, T Zhang, B Gong International Conference on Machine Learning, 3866-3876, 2019 | 157 | 2019 |
| Reshaping visual datasets for domain adaptation B Gong, K Grauman, F Sha Advances in Neural Information Processing Systems 26, 2013 | 151 | 2013 |
| Domain randomization and pyramid consistency: Simulation-to-real generalization without accessing target domain data X Yue, Y Zhang, S Zhao, A Sangiovanni-Vincentelli, K Keutzer, B Gong Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 136 | 2019 |
| Polarnet: An improved grid representation for online lidar point clouds semantic segmentation Y Zhang, Z Zhou, P David, X Yue, Z Xi, B Gong, H Foroosh Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 132 | 2020 |
| Automatic facial expression recognition on a single 3D face by exploring shape deformation B Gong, Y Wang, J Liu, X Tang Proceedings of the 17th ACM international conference on Multimedia, 569-572, 2009 | 126 | 2009 |
| A fast and accurate one-stage approach to visual grounding Z Yang, B Gong, L Wang, W Huang, D Yu, J Luo Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 119 | 2019 |
| Query-Focused Extractive Video Summarization A Sharghi, B Gong, M Shah arXiv preprint arXiv:1607.05177 (Accepted to ECCV 2016), 2016 | 119 | 2016 |
Fei ShaU of Southern CaliforniaVerified email at usc.edu
