Large ai models in health informatics: Applications, challenges, and the future J Qiu, L Li, J Sun, J Peng, P Shi, R Zhang, Y Dong, K Lam, FPW Lo, ... IEEE Journal of Biomedical and Health Informatics (JBHI), 2023 | 72 | 2023 |
Data augmentation alone can improve adversarial training L Li, M Spratling International Conference on Learning Representations (ICLR), 2023 | 38 | 2023 |
Understanding and combating robust overfitting via input loss landscape analysis and regularization L Li, M Spratling Pattern Recognition 136, 109229, 2023 | 24 | 2023 |
Visionfm: a multi-modal multi-task vision foundation model for generalist ophthalmic artificial intelligence J Qiu, J Wu, H Wei, P Shi, M Zhang, Y Sun, L Li, H Liu, H Liu, S Hou, ... arXiv preprint arXiv:2310.04992, 2023 | 7 | 2023 |
One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language Models L Li, H Guan, J Qiu, M Spratling IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 | 3 | 2024 |
OODRobustBench: benchmarking and analyzing adversarial robustness under distribution shift L Li, Y Wang, C Sitawarin, M Spratling International Conference on Machine Learning (ICML) 2024, ICLRW-DMLR 2024, 2024 | 3 | 2024 |
AROID: Improving Adversarial Robustness through Online Instance-wise Data Augmentation L Li, J Qiu, M Spratling International Journal of Computer Vision (IJCV), 2024 | 1 | 2024 |
Improved Adversarial Training Through Adaptive Instance-wise Loss Smoothing L Li, M Spratling arXiv preprint arXiv:2303.14077, 2023 | 1 | 2023 |
Advancing Robots with Greater Dynamic Dexterity: A Large-Scale Multi-View and Multi-Modal Dataset of Human-Human Throw&Catch of Arbitrary Objects L Chen*, J Qiu*, L Li*, X Luo, G Chi, Y Zheng International Journal of Robotics Research (IJRR), 2024 | | 2024 |
OODRobustBench: a benchmark and large-scale analysis of adversarial robustness under distribution shift L Li, Y Wang, C Sitawarin, M Spratling | | 2024 |
Towards Robust Visual Classification through Adversarial Training L Li King's College London, 2024 | | 2024 |
Understanding Deep CNNs via Interpretable Individual Units L Li Imperial College London, 2018 | | 2018 |