| GhostNet: More features from cheap operations K Han, Y Wang, Q Tian, J Guo, C Xu, C Xu CVPR, 1580-1589, 2020 | 1429 | 2020 |
| Pre-trained image processing transformer H Chen, Y Wang, T Guo, C Xu, Y Deng, Z Liu, S Ma, C Xu, C Xu, W Gao CVPR, 12299-12310, 2021 | 903 | 2021 |
| A survey on vision transformer K Han, Y Wang, H Chen, X Chen, J Guo, Z Liu, Y Tang, A Xiao, C Xu, ... IEEE transactions on pattern analysis and machine intelligence 45 (1), 87-110, 2022 | 804* | 2022 |
| Transformer in transformer K Han, A Xiao, E Wu, J Guo, C Xu, Y Wang Advances in Neural Information Processing Systems 34, 15908-15919, 2021 | 717 | 2021 |
| Data-Free Learning of Student Networks H Chen, Y Wang, C Xu, Z Yang, C Liu, B Shi, C Xu, C Xu, Q Tian ICCV, 3514-3522, 2019 | 262 | 2019 |
| Cmt: Convolutional neural networks meet vision transformers J Guo, K Han, H Wu, Y Tang, X Chen, Y Wang, C Xu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 245 | 2022 |
| CARS: Continuous Evolution for Efficient Neural Architecture Search Z Yang, Y Wang, X Chen, B Shi, C Xu, C Xu, Q Tian, C Xu CVPR, 1829-1838, 2020 | 194 | 2020 |
| CNNpack: packing convolutional neural networks in the frequency domain Y Wang, C Xu, S You, D Tao, C Xu NeurIPS, 253-261, 2016 | 183 | 2016 |
| AdderNet: Do we really need multiplications in deep learning? H Chen, Y Wang, C Xu, B Shi, C Xu, Q Tian, C Xu CVPR, 1468-1477, 2020 | 173 | 2020 |
| Autoencoder Inspired Unsupervised Feature Selection K Han, Y Wang, C Zhang, C Li, C Xu IEEE ICASSP, 2941-2945, 2018 | 153 | 2018 |
| Efficient residual dense block search for image super-resolution D Song, C Xu, X Jia, Y Chen, C Xu, Y Wang AAAI, 2020, 2020 | 107 | 2020 |
| Post-training quantization for vision transformer Z Liu, Y Wang, K Han, W Zhang, S Ma, W Gao Advances in Neural Information Processing Systems 34, 28092-28103, 2021 | 105 | 2021 |
| Distilling object detectors via decoupled features J Guo, K Han, Y Wang, H Wu, X Chen, C Xu, C Xu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 103 | 2021 |
| Scop: Scientific control for reliable neural network pruning Y Tang, Y Wang, Y Xu, D Tao, C Xu, C Xu, C Xu Advances in Neural Information Processing Systems 33, 10936-10947, 2020 | 96 | 2020 |
| Hit-Detector: Hierarchical trinity architecture search for object detection J Guo, K Han, Y Wang, C Zhang, Z Yang, H Wu, X Chen, C Xu CVPR, 11405-11414, 2020 | 92 | 2020 |
| Co-evolutionary compression for unpaired image translation H Shu, Y Wang, X Jia, K Han, H Chen, C Xu, Q Tian, C Xu ICCV, 3235-3244, 2019 | 80 | 2019 |
| Manifold regularized dynamic network pruning Y Tang, Y Wang, Y Xu, Y Deng, C Xu, D Tao, C Xu Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 78 | 2021 |
| Patch slimming for efficient vision transformers Y Tang, K Han, Y Wang, C Xu, J Guo, C Xu, D Tao Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 73 | 2022 |
| Searching for low-bit weights in quantized neural networks Z Yang, Y Wang, K Han, C Xu, C Xu, D Tao, C Xu Advances in neural information processing systems 33, 4091-4102, 2020 | 72 | 2020 |
| AdderSR: Towards Energy Efficient Image Super-Resolution D Song, Y Wang, H Chen, C Xu, C Xu, D Tao arXiv preprint arXiv:2009.08891, 2020 | 71 | 2020 |
Chang XuSenior Lecturer, University of SydneyVerified email at sydney.edu.au
