| R-FCN: Object Detection via Region-based Fully Convolutional Networks J Dai, Y Li, K He, J Sun Neural Information Processing Systems (NIPS), 2016 | 7753 | 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 | 6576 | 2017 |
| Deformable DETR: Deformable Transformers for End-to-End Object Detection X Zhu, W Su, L Lu, B Li, X Wang, J Dai ICLR, 2021 | 5194 | 2021 |
| 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 | 3211 | 2019 |
| 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 | 2309 | 2019 |
| 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 | 1829 | 2020 |
| 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 | 1716 | 2016 |
| 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 | 1547 | 2018 |
| 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 | 1360 | 2017 |
| 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 | 1306 | 2015 |
| 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 | 1238 | 2016 |
| Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers Z Li, W Wang, H Li, E Xie, C Sima, T Lu, Y Qiao, J Dai European conference on computer vision, 1-18, 2022 | 1036 | 2022 |
| 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 | 835 | 2017 |
| 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 | 799 | 2017 |
| Internimage: Exploring large-scale vision foundation models with deformable convolutions W Wang, J Dai, Z Chen, Z Huang, Z Li, X Zhu, X Hu, T Lu, L Lu, H Li, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 596 | 2023 |
| 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 | 553 | 2015 |
| 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 | 543 | 2019 |
| Instance-sensitive fully convolutional networks J Dai, K He, Y Li, S Ren, J Sun European Conference on Computer Vision (ECCV), 2016 | 537 | 2016 |
| Exploring cross-image pixel contrast for semantic segmentation W Wang, T Zhou, F Yu, J Dai, E Konukoglu, L Van Gool Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 534 | 2021 |
| Vision Transformer Adapter for Dense Predictions Z Chen, Y Duan, W Wang, J He, T Lu, J Dai, Y Qiao arXiv preprint arXiv:2205.08534, 2022 | 498 | 2022 |
