| Acnet: Strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks X Ding, Y Guo, G Ding, J Han Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 819 | 2019 |
| Collective matrix factorization hashing for multimodal data G Ding, Y Guo, J Zhou Proceedings of the IEEE conference on computer vision and pattern …, 2014 | 704 | 2014 |
| Latent semantic sparse hashing for cross-modal similarity search J Zhou, G Ding, Y Guo Proceedings of the 37th international ACM SIGIR conference on Research …, 2014 | 476 | 2014 |
| Transfer sparse coding for robust image representation M Long, G Ding, J Wang, J Sun, Y Guo, PS Yu Proceedings of the IEEE conference on computer vision and pattern …, 2013 | 236 | 2013 |
| Deep learning-based artificial intelligence model to assist thyroid nodule diagnosis and management: a multicentre diagnostic study S Peng, Y Liu, W Lv, L Liu, Q Zhou, H Yang, J Ren, G Liu, X Wang, ... The Lancet Digital Health 3 (4), e250-e259, 2021 | 229 | 2021 |
| Centripetal sgd for pruning very deep convolutional networks with complicated structure X Ding, G Ding, Y Guo, J Han Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 224 | 2019 |
| Large-scale cross-modality search via collective matrix factorization hashing G Ding, Y Guo, J Zhou, Y Gao IEEE Transactions on Image Processing 25 (11), 5427-5440, 2016 | 215 | 2016 |
| Resrep: Lossless cnn pruning via decoupling remembering and forgetting X Ding, T Hao, J Tan, J Liu, J Han, Y Guo, G Ding Proceedings of the IEEE/CVF international conference on computer vision …, 2021 | 208 | 2021 |
| Global sparse momentum sgd for pruning very deep neural networks X Ding, X Zhou, Y Guo, J Han, J Liu Advances in Neural Information Processing Systems 32, 2019 | 192 | 2019 |
| Unsupervised deep video hashing via balanced code for large-scale video retrieval G Wu, J Han, Y Guo, L Liu, G Ding, Q Ni, L Shao IEEE Transactions on Image Processing 28 (4), 1993-2007, 2018 | 158 | 2018 |
| Approximated oracle filter pruning for destructive cnn width optimization X Ding, G Ding, Y Guo, J Han, C Yan International Conference on Machine Learning, 1607-1616, 2019 | 141 | 2019 |
| Zero-shot learning with transferred samples Y Guo, G Ding, J Han, Y Gao IEEE Transactions on Image Processing 26 (7), 3277-3290, 2017 | 123 | 2017 |
| Transductive zero-shot recognition via shared model space learning Y Guo, G Ding, X Jin, J Wang Proceedings of the AAAI conference on artificial intelligence 30 (1), 2016 | 116 | 2016 |
| Grouping attribute recognition for pedestrian with joint recurrent learning. X Zhao, L Sang, G Ding, Y Guo, X Jin IJCAI 2018, 27th, 2018 | 108 | 2018 |
| Learning to hash with optimized anchor embedding for scalable retrieval Y Guo, G Ding, L Liu, J Han, L Shao IEEE Transactions on Image Processing 26 (3), 1344-1354, 2017 | 108 | 2017 |
| From brain science to artificial intelligence J Fan, L Fang, J Wu, Y Guo, Q Dai Engineering 6 (3), 248-252, 2020 | 104 | 2020 |
| Panda: A gigapixel-level human-centric video dataset X Wang, X Zhang, Y Zhu, Y Guo, X Yuan, L Xiang, Z Wang, G Ding, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 95 | 2020 |
| Synthesizing Samples for Zero-shot Learning. Y Guo, G Ding, J Han, Y Gao IJCAI 1, 2, 2017 | 88 | 2017 |
| Analog optical computing for artificial intelligence J Wu, X Lin, Y Guo, J Liu, L Fang, S Jiao, Q Dai Engineering 10, 133-145, 2022 | 82 | 2022 |
| Secret: Self-consistent pseudo label refinement for unsupervised domain adaptive person re-identification T He, L Shen, Y Guo, G Ding, Z Guo Proceedings of the AAAI conference on artificial intelligence 36 (1), 879-887, 2022 | 77 | 2022 |
Ding GuiguangTsinghua UniversityVerified email at tsinghua.edu.cn
