| Objects as points X Zhou, D Wang, P Krähenbühl arXiv preprint arXiv:1904.07850, 2019 | 4587* | 2019 |
| Deep layer aggregation F Yu, D Wang, E Shelhamer, T Darrell Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 1706 | 2018 |
| Tent: Fully test-time adaptation by entropy minimization D Wang, E Shelhamer, S Liu, B Olshausen, T Darrell arXiv preprint arXiv:2006.10726, 2020 | 1086 | 2020 |
| Visda: The visual domain adaptation challenge X Peng, B Usman, N Kaushik, J Hoffman, D Wang, K Saenko arXiv preprint arXiv:1710.06924, 2017 | 1086* | 2017 |
| Fcns in the wild: Pixel-level adversarial and constraint-based adaptation J Hoffman, D Wang, F Yu, T Darrell arXiv preprint arXiv:1612.02649, 2016 | 906 | 2016 |
| Contrastive Test-Time Adaptation D Chen, D Wang, T Darrell, S Ebrahimi Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 277 | 2022 |
| Multiple granularity descriptors for fine-grained categorization D Wang, Z Shen, J Shao, W Zhang, X Xue, Z Zhang Proceedings of the IEEE international conference on computer vision, 2399-2406, 2015 | 270 | 2015 |
| Joint monocular 3D vehicle detection and tracking HN Hu, QZ Cai, D Wang, J Lin, M Sun, P Krahenbuhl, T Darrell, F Yu Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 269 | 2019 |
| Deep object-centric policies for autonomous driving D Wang, C Devin, QZ Cai, F Yu, T Darrell 2019 International Conference on Robotics and Automation (ICRA), 8853-8859, 2019 | 129 | 2019 |
| Back to the Source: Diffusion-Driven Adaptation To Test-Time Corruption J Gao, J Zhang, X Liu, T Darrell, E Shelhamer, D Wang Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 94 | 2023 |
| Monocular plan view networks for autonomous driving D Wang, C Devin, QZ Cai, P Krähenbühl, T Darrell 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2019 | 89 | 2019 |
| Weakly supervised semantic segmentation for social images W Zhang, S Zeng, D Wang, X Xue Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2015 | 77 | 2015 |
| Actnn: Reducing training memory footprint via 2-bit activation compressed training J Chen, L Zheng, Z Yao, D Wang, I Stoica, M Mahoney, J Gonzalez International Conference on Machine Learning, 1803-1813, 2021 | 72 | 2021 |
| Codenet: Efficient deployment of input-adaptive object detection on embedded fpgas Q Huang, D Wang, Z Dong, Y Gao, Y Cai, T Li, B Wu, K Keutzer, ... The 2021 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays …, 2021 | 65* | 2021 |
| BEV-Seg: Bird's Eye View Semantic Segmentation Using Geometry and Semantic Point Cloud MH Ng, K Radia, J Chen, D Wang, I Gog, JE Gonzalez arXiv preprint arXiv:2006.11436, 2020 | 65 | 2020 |
| On-target adaptation D Wang, S Liu, S Ebrahimi, E Shelhamer, T Darrell arXiv preprint arXiv:2109.01087, 2021 | 41* | 2021 |
| Text-Guided Foundation Model Adaptation for Pathological Image Classification Y Zhang, J Gao, M Zhou, X Wang, Y Qiao, S Zhang, D Wang International Conference on Medical Image Computing and Computer-Assisted …, 2023 | 36 | 2023 |
| Blurring the line between structure and learning to optimize and adapt receptive fields E Shelhamer, D Wang, T Darrell arXiv preprint arXiv:1904.11487, 2019 | 34 | 2019 |
| Fighting Gradients with Gradients: Dynamic Defenses against Adversarial Attacks D Wang, A Ju, E Shelhamer, D Wagner, T Darrell arXiv preprint arXiv:2105.08714, 2021 | 33 | 2021 |
| A Real-world Dataset and Benchmark For Foundation Model Adaptation in Medical Image Classification D Wang, X Wang, L Wang, M Li, Q Da, X Liu, X Gao, J Shen, J He, T Shen, ... Scientific Data 10 (1), 574, 2023 | 30 | 2023 |