| R-FCN: Object Detection via Region-based Fully Convolutional Networks J Dai, Y Li, K He, J Sun Neural Information Processing Systems (NIPS), 2016 | 4680 | 2016 |
| Deformable Convolutional Networks J Dai, H Qi, Y Xiong, Y Li, G Zhang, H Hu, Y Wei Computer Vision (ICCV), 2017 IEEE International Conference on, 2017 | 2479 | 2017 |
| Instance-aware Semantic Segmentation via Multi-task Network Cascades J Dai, K He, J Sun Computer Vision and Pattern Recognition (CVPR), 2016 IEEE Conference on, 2016 | 1236 | 2016 |
| Fully Convolutional Instance-aware Semantic Segmentation Y Li, H Qi, J Dai, X Ji, Y Wei Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on, 2017 | 887 | 2017 |
| Mmdetection: Open mmlab detection toolbox and benchmark K Chen, J Wang, J Pang, Y Cao, Y Xiong, X Li, S Sun, W Feng, Z Liu, J Xu, ... arXiv preprint arXiv:1906.07155, 2019 | 817 | 2019 |
| Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation J Dai, K He, J Sun Computer Vision (ICCV), 2015 IEEE International Conference on, 2015 | 815 | 2015 |
| Relation networks for object detection H Hu, J Gu, Z Zhang, J Dai, Y Wei Computer Vision and Pattern Recognition (CVPR), 2018 IEEE Conference on, 2018 | 745 | 2018 |
| Deformable convnets v2: More deformable, better results X Zhu, H Hu, S Lin, J Dai Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 724 | 2019 |
| ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation D Lin, J Dai, J Jia, K He, J Sun Computer Vision and Pattern Recognition (CVPR), 2016 IEEE Conference on, 2016 | 600 | 2016 |
| Vl-bert: Pre-training of generic visual-linguistic representations W Su, X Zhu, Y Cao, B Li, L Lu, F Wei, J Dai The International Conference on Learning Representations (ICLR), 2020 | 542 | 2020 |
| Convolutional feature masking for joint object and stuff segmentation J Dai, K He, J Sun Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on, 2015 | 446 | 2015 |
| Deformable DETR: Deformable Transformers for End-to-End Object Detection X Zhu, W Su, L Lu, B Li, X Wang, J Dai ICLR, 2021 | 421 | 2021 |
| Flow-Guided Feature Aggregation for Video Object Detection X Zhu, Y Wang, J Dai, L Yuan, Y Wei Computer Vision (ICCV), 2017 IEEE International Conference on, 2017 | 411 | 2017 |
| Deep Feature Flow for Video Recognition X Zhu, Y Xiong, J Dai, L Yuan, Y Wei Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on, 2017 | 408 | 2017 |
| Instance-sensitive fully convolutional networks J Dai, K He, Y Li, S Ren, J Sun European Conference on Computer Vision (ECCV), 2016 | 385 | 2016 |
| Multifeature-based high-resolution palmprint recognition J Dai, J Zhou IEEE Transactions on Pattern Analysis and Machine Intelligence 33 (5), 945-957, 2010 | 172 | 2010 |
| Towards high performance video object detection X Zhu, J Dai, L Yuan, Y Wei Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 163 | 2018 |
| Robust and efficient ridge-based palmprint matching J Dai, J Feng, J Zhou IEEE transactions on pattern analysis and machine intelligence 34 (8), 1618-1632, 2011 | 139 | 2011 |
| An empirical study of spatial attention mechanisms in deep networks X Zhu, D Cheng, Z Zhang, S Lin, J Dai International Conference on Computer Vision (ICCV), 2019 | 117 | 2019 |
| Cosegmentation and cosketch by unsupervised learning J Dai, Y Wu, J Zhou, SC Zhu Proceedings of the IEEE International Conference on Computer Vision, 1305-1312, 2013 | 87 | 2013 |
Xizhou ZhuSenseTime Research在 sensetime.com 的电子邮件经过验证
