| Towards Understanding Regularization in Batch Normalization P Luo*, X Wang*, W Shao*, Z Peng (*Equal Contribution) ICLR 2019, 2018 | 263 | 2018 |
| Sphinx: The joint mixing of weights, tasks, and visual embeddings for multi-modal large language models Z Lin, C Liu, R Zhang, P Gao, L Qiu, H Xiao, H Qiu, C Lin, W Shao, ... arXiv preprint arXiv:2311.07575, 2023 | 149 | 2023 |
| Gpt4roi: Instruction tuning large language model on region-of-interest S Zhang, P Sun, S Chen, M Xiao, W Shao, W Zhang, Y Liu, K Chen, P Luo arXiv preprint arXiv:2307.03601, 2023 | 144 | 2023 |
| Lvlm-ehub: A comprehensive evaluation benchmark for large vision-language models P Xu, W Shao, K Zhang, P Gao, S Liu, M Lei, F Meng, S Huang, Y Qiao, ... arXiv preprint arXiv:2306.09265, 2023 | 133 | 2023 |
| Omniquant: Omnidirectionally calibrated quantization for large language models W Shao, M Chen, Z Zhang, P Xu, L Zhao, Z Li, K Zhang, P Gao, Y Qiao, ... arXiv preprint arXiv:2308.13137, 2023 | 103 | 2023 |
| What makes for end-to-end object detection? P Sun, Y Jiang, E Xie, W Shao, Z Yuan, C Wang, P Luo International Conference on Machine Learning, 9934-9944, 2021 | 98 | 2021 |
| Imagebind-llm: Multi-modality instruction tuning J Han, R Zhang, W Shao, P Gao, P Xu, H Xiao, K Zhang, C Liu, S Wen, ... arXiv preprint arXiv:2309.03905, 2023 | 86 | 2023 |
| SSN: Learning Sparse Switchable Normalization via SparsestMax W Shao, J Li, J Ren, R Zhang, X Wang, P Luo International Journal of Computer Vision, 2019 | 71 | 2019 |
| SSN: Learning Sparse Switchable Normalization via SparsestMax W Shao*, T Meng*, J Li, R Zhang, Y Li, X Wang, ... CVPR 2019, arXiv preprint arXiv:1903.03793, 2019 | 71 | 2019 |
| Sphinx-x: Scaling data and parameters for a family of multi-modal large language models P Gao, R Zhang, C Liu, L Qiu, S Huang, W Lin, S Zhao, S Geng, Z Lin, ... arXiv preprint arXiv:2402.05935, 2024 | 68 | 2024 |
| Rethinking the pruning criteria for convolutional neural network Z Huang, W Shao, X Wang, L Lin, P Luo Advances in Neural Information Processing Systems 34, 16305-16318, 2021 | 52 | 2021 |
| Differentiable Learning-to-Group Channels via Groupable Convolutional Neural Networks Z Zhaoyang, L Jingyu, S Wenqi, P Zhanglin, Z Ruimao, W Xiaogang, ... ICCV 2019, 2019 | 46 | 2019 |
| Differentiable Dynamic Quantization with Mixed Precision and Adaptive Resolution Z Zhaoyang, S Wenqi, G Jinwei, W Xiaogang, L Ping ICML 2021, 2021 | 35 | 2021 |
| Mmt-bench: A comprehensive multimodal benchmark for evaluating large vision-language models towards multitask agi K Ying, F Meng, J Wang, Z Li, H Lin, Y Yang, H Zhang, W Zhang, Y Lin, ... arXiv preprint arXiv:2404.16006, 2024 | 29 | 2024 |
| Tiny lvlm-ehub: Early multimodal experiments with bard W Shao, Y Hu, P Gao, M Lei, K Zhang, F Meng, P Xu, S Huang, H Li, ... arXiv preprint arXiv:2308.03729, 2023 | 28 | 2023 |
| Not All Models Are Equal: Predicting Model Transferability in a Self-challenging Fisher Space W Shao, X Zhao, Y Ge, Z Zhang, L Yang, X Wang, Y Shan, P Luo ECCV 2022, arXiv preprint arXiv:2207.03036, 2022 | 28 | 2022 |
| Differentiable Dynamic Normalization for Learning Deep Representation P Luo, P Zhanglin, S Wenqi, Z Ruimao, R Jiamin, W Lingyun ICML 2019, http://proceedings.mlr.press/v97/luo19a.html, 2019 | 28 | 2019 |
| DiffRate: Differentiable Compression Rate for Efficient Vision Transformers M Chen, W Shao, P Xu, M Lin, K Zhang, F Chao, R Ji, Y Qiao, P Luo ICCV23, arXiv preprint arXiv:2305.17997, 2023 | 27 | 2023 |
| Beyond one-to-one: Rethinking the referring image segmentation Y Hu, Q Wang, W Shao, E Xie, Z Li, J Han, P Luo Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 24 | 2023 |
| Convolution-weight-distribution assumption: Rethinking the criteria of channel pruning Z Huang*, W Shao*, X Wang, L Lin, P Luo NeurIPS 2021, arXiv preprint arXiv:2004.11627, 2020 | 24 | 2020 |
Ping Luo (羅平)Associate Professor, The University of Hong KongVerified email at hku.hk
