| Ternary weight networks B Liu, F Li, X Wang, B Zhang, J Yan ICASSP 2013 (arXiv:1605.04711), 2016 | 1094* | 2016 |
| SCRDet: Towards more robust detection for small, cluttered and rotated objects X Yang, J Yang, J Yan, Y Zhang, T Zhang, Z Guo, X Sun, K Fu ICCV 2019, 2019 | 565 | 2019 |
| R3Det: Refined single-stage detector with feature refinement for rotating object X Yang, J Yan, Z Feng, T He AAAI 2021, 2021 | 459 | 2021 |
| Image matching from handcrafted to deep features: A survey J Ma, X Jiang, A Fan, J Jiang, J Yan IJCV 2021, 2021 | 452 | 2021 |
| Learning time series associated event sequences with recurrent point process networks S Xiao, J Yan, M Farajtabar, L Song, X Yang, H Zha IEEE TNNLS 2019, 2019 | 337* | 2019 |
| Unsupervised deep learning for optical flow estimation Z Ren, J Yan, B Ni, B Liu, X Yang, H Zha AAAI 2017, 2017 | 309 | 2017 |
| Arbitrary-oriented object detection with circular smooth label X Yang, J Yan ECCV 2020, 2020 | 265 | 2020 |
| Modeling the intensity function of point process via recurrent neural networks S Xiao, J Yan, X Yang, H Zha, S Chu AAAI 2017, 2017 | 248 | 2017 |
| Deep spectral clustering using dual autoencoder network X Yang, C Deng, F Zheng, J Yan, W Liu CVPR 2019, 2019 | 230 | 2019 |
| Visual saliency detection via sparsity pursuit J Yan, M Zhu, H Liu, Y Liu IEEE Signal Processing Letters 17 (8), 739-742, 2010 | 207 | 2010 |
| Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks X Pan, P Rijnbeek, J Yan, HB Shen BMC genomics 2018, 2018 | 203 | 2018 |
| Person re-identification with correspondence structure learning Y Shen, W Lin, J Yan, M Xu, J Wu, J Wang ICCV 2015, 2015 | 194 | 2015 |
| Learning modulated loss for rotated object detection W Qian, X Yang, S Peng, Y Guo, J Yan AAAI 2021, 2021 | 192 | 2021 |
| Multi-graph matching via affinity optimization with graduated consistency regularization J Yan, M Cho, H Zha, X Yang, SM Chu IEEE TPAMI 2016, 2016 | 181 | 2016 |
| Learning combinatorial embedding networks for deep graph matching R Wang, J Yan, X Yang ICCV 2019, 2019 | 179 | 2019 |
| Wasserstein learning of deep generative point process models S Xiao, M Farajtabar, X Ye, J Yan, L Song, H Zha NIPS 2017, 2017 | 165 | 2017 |
| A short survey of recent advances in graph matching J Yan, XC Yin, W Lin, C Deng, H Zha, X Yang ICMR 2016, 2016 | 165 | 2016 |
| Rethinking rotated object detection with gaussian wasserstein distance loss X Yang, J Yan, Q Ming, W Wang, X Zhang, Q Tian ICML 2021, 2021 | 159 | 2021 |
| Dense Label Encoding for Boundary Discontinuity Free Rotation Detection X Yang, L Hou, Y Zhou, W Wang, J Yan CVPR 2021, 2021 | 143 | 2021 |
| Generalizing Face Forgery Detection with High-frequency Features Y Luo, Y Zhang, J Yan, W Liu CVPR 2021, 2021 | 125 | 2021 |
Xiaokang YangFIEEE, Shanghai Jiao Tong UniversityVerified email at sjtu.edu.cn
