Dab-detr: Dynamic anchor boxes are better queries for detr S Liu, F Li, H Zhang, X Yang, X Qi, H Su, J Zhu, L Zhang arXiv preprint arXiv:2201.12329, 2022 | 493 | 2022 |
Bag of tricks for adversarial training T Pang, X Yang, Y Dong, H Su, J Zhu arXiv preprint arXiv:2010.00467, 2020 | 299* | 2020 |
Benchmarking adversarial robustness on image classification Y Dong, QA Fu, X Yang, T Pang, H Su, Z Xiao, J Zhu proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 278 | 2020 |
Face anti-spoofing: Model matters, so does data X Yang, W Luo, L Bao, Y Gao, D Gong, S Zheng, Z Li, W Liu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 248 | 2019 |
Query2label: A simple transformer way to multi-label classification S Liu, L Zhang, X Yang, H Su, J Zhu arXiv preprint arXiv:2107.10834, 2021 | 160 | 2021 |
Boosting adversarial training with hypersphere embedding T Pang, X Yang, Y Dong, K Xu, J Zhu, H Su Advances in Neural Information Processing Systems 33, 7779-7792, 2020 | 147 | 2020 |
Robustness and accuracy could be reconcilable by (proper) definition T Pang, M Lin, X Yang, J Zhu, S Yan International Conference on Machine Learning, 17258-17277, 2022 | 94 | 2022 |
Black-box detection of backdoor attacks with limited information and data Y Dong, X Yang, Z Deng, T Pang, Z Xiao, H Su, J Zhu Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 93 | 2021 |
Towards face encryption by generating adversarial identity masks X Yang, Y Dong, T Pang, H Su, J Zhu, Y Chen, H Xue Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 72 | 2021 |
Exploring memorization in adversarial training Y Dong, K Xu, X Yang, T Pang, Z Deng, H Su, J Zhu arXiv preprint arXiv:2106.01606, 2021 | 64 | 2021 |
On evaluating adversarial robustness of large vision-language models Y Zhao, T Pang, C Du, X Yang, C Li, NMM Cheung, M Lin Advances in Neural Information Processing Systems 36, 2024 | 54 | 2024 |
Libre: A practical bayesian approach to adversarial detection Z Deng, X Yang, S Xu, H Su, J Zhu Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 54 | 2021 |
A recipe for watermarking diffusion models Y Zhao, T Pang, C Du, X Yang, NM Cheung, M Lin arXiv preprint arXiv:2303.10137, 2023 | 53 | 2023 |
Design and interpretation of universal adversarial patches in face detection X Yang, F Wei, H Zhang, J Zhu Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 39 | 2020 |
Benchmarking robustness of 3d object detection to common corruptions Y Dong, C Kang, J Zhang, Z Zhu, Y Wang, X Yang, H Su, X Wei, J Zhu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 36 | 2023 |
How Robust is Google's Bard to Adversarial Image Attacks? Y Dong, H Chen, J Chen, Z Fang, X Yang, Y Zhang, Y Tian, H Su, J Zhu arXiv preprint arXiv:2309.11751, 2023 | 34 | 2023 |
Unsupervised part segmentation through disentangling appearance and shape S Liu, L Zhang, X Yang, H Su, J Zhu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 26 | 2021 |
Boosting transferability of targeted adversarial examples via hierarchical generative networks X Yang, Y Dong, T Pang, H Su, J Zhu European Conference on Computer Vision, 725-742, 2022 | 25 | 2022 |
Robfr: Benchmarking adversarial robustness on face recognition X Yang, D Yang, Y Dong, H Su, W Yu, J Zhu arXiv preprint arXiv:2007.04118, 2020 | 25* | 2020 |
A comprehensive study on robustness of image classification models: Benchmarking and rethinking C Liu, Y Dong, W Xiang, X Yang, H Su, J Zhu, Y Chen, Y He, H Xue, ... arXiv preprint arXiv:2302.14301, 2023 | 23 | 2023 |