| R-FCN: Object Detection via Region-based Fully Convolutional Networks J Dai, Y Li, K He, J Sun Neural Information Processing Systems (NIPS), 2016 | 8061 | 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 | 7142 | 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 | 6201 | 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 | 3514 | 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 | 2602 | 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 | 1941 | 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 | 1748 | 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 | 1609 | 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 | 1388 | 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 | 1334 | 2015 |
| BEVFormer: Learning Bird's-Eye-View Representation From LiDAR-Camera Via Spatiotemporal Transformers Z Li, W Wang, H Li, E Xie, C Sima, T Lu, Q Yu, J Dai IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024 | 1320 | 2024 |
| 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 | 1292 | 2016 |
| 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 | 866 | 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 | 840 | 2023 |
| 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 | 839 | 2017 |
| 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 | 622 | 2022 |
| 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 | 621 | 2019 |
| Planning-oriented autonomous driving Y Hu, J Yang, L Chen, K Li, C Sima, X Zhu, S Chai, S Du, T Lin, W Wang, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 615 | 2023 |
| Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks Z Chen, J Wu, W Wang, W Su, G Chen, S Xing, M Zhong, Q Zhang, X Zhu, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 611* | 2024 |
| 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 | 598 | 2021 |
