| Jointly optimize data augmentation and network training: Adversarial data augmentation in human pose estimation X Peng*, Z Tang*, F Yang, RS Feris, D Metaxas Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 269 | 2018 |
| Semantic-guided multi-attention localization for zero-shot learning Y Zhu, J Xie, Z Tang, X Peng, A Elgammal Advances in Neural Information Processing Systems 32, 2019 | 160 | 2019 |
| Quantized densely connected u-nets for efficient landmark localization Z Tang, X Peng, S Geng, L Wu, S Zhang, D Metaxas Proceedings of the European conference on computer vision (ECCV), 339-354, 2018 | 159 | 2018 |
| OnlineAugment: Online data augmentation with less domain knowledge Z Tang, Y Gao, L Karlinsky, P Sattigeri, R Feris, D Metaxas Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 77 | 2020 |
| Crossnorm and selfnorm for generalization under distribution shifts Z Tang, Y Gao, Y Zhu, Z Zhang, M Li, DN Metaxas Proceedings of the IEEE/CVF International Conference on Computer Vision, 52-61, 2021 | 73* | 2021 |
| Towards efficient u-nets: A coupled and quantized approach Z Tang, X Peng, K Li, DN Metaxas IEEE transactions on pattern analysis and machine intelligence 42 (8), 2038-2050, 2019 | 66 | 2019 |
| Visual prompt tuning for test-time domain adaptation Y Gao, X Shi, Y Zhu, H Wang, Z Tang, X Zhou, M Li, DN Metaxas arXiv preprint arXiv:2210.04831, 2022 | 65 | 2022 |
| CU-Net: Coupled U-Nets Z Tang, X Peng, S Geng, Y Zhu, D Metaxas BMVC 2018, 2018 | 52 | 2018 |
| Toward marker-free 3D pose estimation in lifting: A deep multi-view solution R Mehrizi, X Peng, Z Tang, X Xu, D Metaxas, K Li 2018 13th IEEE international conference on automatic face & gesture …, 2018 | 41 | 2018 |
| Are multimodal models robust to image and text perturbations? J Qiu, Y Zhu, X Shi, F Wenzel, Z Tang, D Zhao, B Li, M Li arXiv preprint arXiv:2212.08044, 2022 | 26* | 2022 |
| Enabling data diversity: efficient automatic augmentation via regularized adversarial training Y Gao, Z Tang, M Zhou, D Metaxas International Conference on Information Processing in Medical Imaging, 85-97, 2021 | 26 | 2021 |
| Adatransform: Adaptive data transformation Z Tang, X Peng, T Li, Y Zhu, DN Metaxas Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 23 | 2019 |
| Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model Z Zhong, Z Tang, T He, H Fang, C Yuan The International Conference on Learning Representations (ICLR), 2024 | 16 | 2024 |
| Learning Multimodal Data Augmentation in Feature Space Z Liu, Z Tang, X Shi, A Zhang, M Li, A Shrivastava, AG Wilson The International Conference on Learning Representations (ICLR), 2023 | 14 | 2023 |
| Face clustering in videos with proportion prior Z Tang, Y Zhang, Z Li, H Lu Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015 | 13 | 2015 |
| The importance of 3D motion trajectories for computer-based sign recognition M Dilsizian, Z Tang, D Metaxas, M Huenerfauth, C Neidle Proceedings of the 7th Workshop on the Representation and Processing of Sign …, 2016 | 12 | 2016 |
| A coupled hidden conditional random field model for simultaneous face clustering and naming in videos Y Zhang, Z Tang, B Wu, Q Ji, H Lu IEEE Transactions on Image Processing 25 (12), 5780-5792, 2016 | 11 | 2016 |
| Automatic face annotation in TV series by video/script alignment Y Zhang, Z Tang, C Zhang, J Liu, H Lu Neurocomputing 152, 316-321, 2015 | 10 | 2015 |
| AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models Z Tang, H Fang, S Zhou, T Yang, Z Zhong, T Hu, K Kirchhoff, G Karypis The International Conference on Automated Machine Learning (AutoML), 2024 | 4 | 2024 |
| Video face naming using global sequence alignment Z Tang, Y Zhang, S Qiu, H Lu 2014 IEEE International Conference on Image Processing (ICIP), 353-357, 2014 | 2 | 2014 |
