| Rethinking network design and local geometry in point cloud: A simple residual MLP framework X Ma, C Qin, H You, H Ran, Y Fu arXiv preprint arXiv:2202.07123, 2022 | 421 | 2022 |
| Pointdan: A multi-scale 3d domain adaption network for point cloud representation C Qin, H You, L Wang, CCJ Kuo, Y Fu Advances in Neural Information Processing Systems 32, 2019 | 196 | 2019 |
| Neural pruning via growing regularization H Wang, C Qin, Y Zhang, Y Fu arXiv preprint arXiv:2012.09243, 2020 | 123 | 2020 |
| Face recognition: too bias, or not too bias? JP Robinson, G Livitz, Y Henon, C Qin, Y Fu, S Timoner Proceedings of the ieee/cvf conference on computer vision and pattern …, 2020 | 117 | 2020 |
| Semi-supervised hyperspectral image classification via spatial-regulated self-training Y Wu, G Mu, C Qin, Q Miao, W Ma, X Zhang Remote Sensing 12 (1), 159, 2020 | 82 | 2020 |
| Self-directed online machine learning for topology optimization C Deng, Y Wang, C Qin, Y Fu, W Lu Nature communications 13 (1), 388, 2022 | 74 | 2022 |
| Context reasoning attention network for image super-resolution Y Zhang, D Wei, C Qin, H Wang, H Pfister, Y Fu Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 71 | 2021 |
| Contradictory Structure Learning for Semi-supervised Domain Adaptation C Qin, L Wang, Q Ma, Y Yin, H Wang, Y Fu Society for Industrial and Applied Mathematics, 2020 | 55 | 2020 |
| Recent advances on neural network pruning at initialization H Wang, C Qin, Y Bai, Y Zhang, Y Fu arXiv preprint arXiv:2103.06460, 2021 | 50 | 2021 |
| Aligned structured sparsity learning for efficient image super-resolution Y Zhang, H Wang, C Qin, Y Fu Advances in Neural Information Processing Systems 34, 2695-2706, 2021 | 48 | 2021 |
| Learning efficient image super-resolution networks via structure-regularized pruning Y Zhang, H Wang, C Qin, Y Fu International conference on learning representations, 2021 | 41 | 2021 |
| Emerging paradigms of neural network pruning H Wang, C Qin, Y Zhang, Y Fu arXiv preprint arXiv:2103.06460 8, 2021 | 39 | 2021 |
| Unicontrol: A unified diffusion model for controllable visual generation in the wild C Qin, S Zhang, N Yu, Y Feng, X Yang, Y Zhou, H Wang, JC Niebles, ... arXiv preprint arXiv:2305.11147, 2023 | 36 | 2023 |
| Dual relation semi-supervised multi-label learning L Wang, Y Liu, C Qin, G Sun, Y Fu Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 6227-6234, 2020 | 35 | 2020 |
| Image as set of points X Ma, Y Zhou, H Wang, C Qin, B Sun, C Liu, Y Fu arXiv preprint arXiv:2303.01494, 2023 | 34 | 2023 |
| Generatively inferential co-training for unsupervised domain adaptation C Qin, L Wang, Y Zhang, Y Fu Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 33 | 2019 |
| Hive: Harnessing human feedback for instructional visual editing S Zhang, X Yang, Y Feng, C Qin, CC Chen, N Yu, Z Chen, H Wang, ... arXiv preprint arXiv:2303.09618, 2023 | 31 | 2023 |
| Semi-supervised dual relation learning for multi-label classification L Wang, Y Liu, H Di, C Qin, G Sun, Y Fu IEEE Transactions on Image Processing 30, 9125-9135, 2021 | 22 | 2021 |
| Balancing biases and preserving privacy on balanced faces in the wild JP Robinson, C Qin, Y Henon, S Timoner, Y Fu IEEE Transactions on Image Processing, 2023 | 18 | 2023 |
| Why is the state of neural network pruning so confusing? on the fairness, comparison setup, and trainability in network pruning H Wang, C Qin, Y Bai, Y Fu arXiv preprint arXiv:2301.05219, 2023 | 15 | 2023 |
