| Shufflenet: An extremely efficient convolutional neural network for mobile devices X Zhang, X Zhou, M Lin, J Sun Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 9662 | 2018 |
| Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients S Zhou, Y Wu, Z Ni, X Zhou, H Wen, Y Zou arXiv preprint arXiv:1606.06160, 2016 | 2597 | 2016 |
| East: an efficient and accurate scene text detector X Zhou, C Yao, H Wen, Y Wang, S Zhou, W He, J Liang Proceedings of the IEEE conference on Computer Vision and Pattern …, 2017 | 2150 | 2017 |
| Scene text detection via holistic, multi-channel prediction C Yao, X Bai, N Sang, X Zhou, S Zhou, Z Cao arXiv preprint arXiv:1606.09002, 2016 | 300 | 2016 |
| Dpgn: Distribution propagation graph network for few-shot learning L Yang, L Li, Z Zhang, X Zhou, E Zhou, Y Liu Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 279 | 2020 |
| Learning delicate local representations for multi-person pose estimation Y Cai, Z Wang, Z Luo, B Yin, A Du, H Wang, X Zhang, X Zhou, E Zhou, ... Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 253 | 2020 |
| Effective quantization methods for recurrent neural networks Q He, H Wen, S Zhou, Y Wu, C Yao, X Zhou, Y Zou arXiv preprint arXiv:1611.10176, 2016 | 94 | 2016 |
| Feature space singularity for out-of-distribution detection H Huang, Z Li, L Wang, S Chen, B Dong, X Zhou arXiv preprint arXiv:2011.14654, 2020 | 78 | 2020 |
| Exploiting local structures with the kronecker layer in convolutional networks S Zhou, JN Wu, Y Wu, X Zhou arXiv preprint arXiv:1512.09194, 2015 | 46 | 2015 |
| Modeling paying behavior in game social networks Z Fang, X Zhou, J Tang, W Shao, ACM Fong, L Sun, Y Ding, L Zhou, J Luo Proceedings of the 23rd ACM International Conference on Conference on …, 2014 | 35 | 2014 |
| Learning to run with actor-critic ensemble Z Huang, S Zhou, BE Zhuang, X Zhou arXiv preprint arXiv:1712.08987, 2017 | 34 | 2017 |
| Icdar 2015 text reading in the wild competition X Zhou, S Zhou, C Yao, Z Cao, Q Yin arXiv preprint arXiv:1506.03184, 2015 | 26 | 2015 |
| Incidental scene text understanding: Recent progresses on icdar 2015 robust reading competition challenge 4 C Yao, J Wu, X Zhou, C Zhang, S Zhou, Z Cao, Q Yin arXiv preprint arXiv:1511.09207, 2015 | 16 | 2015 |
| Training bit fully convolutional network for fast semantic segmentation H Wen, S Zhou, Z Liang, Y Zhang, D Feng, X Zhou, C Yao arXiv preprint arXiv:1612.00212, 2016 | 11 | 2016 |
| Kimi k1. 5: Scaling reinforcement learning with llms K Team, A Du, B Gao, B Xing, C Jiang, C Chen, C Li, C Xiao, C Du, C Liao, ... arXiv preprint arXiv:2501.12599, 2025 | 7 | 2025 |
| DoReFa-net: Training low Bitwidth convolutional neural networks with low Bitwidth gradients. arXiv preprint. 2016 S Zhou, Y Wu, Z Ni, X Zhou, H Wen, Y Zou arXiv preprint arXiv:1606.06160, 0 | 6 | |
| Determination of a geographical location of a user S Zhou, X Zhou, WU Yuxin, WU Jianan US Patent 10,657,669, 2020 | 5 | 2020 |
| Res-steps-net for multi-person pose estimation Y Cai, Z Wang, B Yin, R Yin, A Du, Z Luo, Z Li, X Zhou, G Yu, E Zhou, ... Joint COCO and Mapillary Workshop at ICCV, 3, 2019 | 5 | 2019 |
| Training low bitwidth convolutional neural networks with low bitwidth gradients S Zhou, Y Wu, Z Ni, X Zhou, H Wen, YZ DoReFa-Net arXiv preprint arXiv:1606.06160, 2016 | 5 | 2016 |
| Joint COCO and Mapillary Workshop at ICCV 2019: Panoptic Segmentation Challenge Track Technical Report: Explore Context Relation for Panoptic Segmentation S Wang, T Liu, H Liu, Y Ma, Z Li, Z Wang, X Zhou, G Yu, E Zhou, X Zhang, ... ICCV workshop, 2019 | 4* | 2019 |
Shuchang ZhouMegvii Inc.Correu electrònic verificat a megvii.com