| mPLUG-Owl: Modularization empowers large language models with multimodality Q Ye, H Xu, G Xu, J Ye, M Yan, Y Zhou, J Wang, A Hu, P Shi, Y Shi, C Li, ... arXiv preprint arXiv:2304.14178, 2023 | 831 | 2023 |
| Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond G Yang, Q Ye, J Xia Information Fusion, 2021 | 601 | 2021 |
| mplug-owl2: Revolutionizing multi-modal large language model with modality collaboration Q Ye, H Xu, J Ye, M Yan, H Liu, Q Qian, J Zhang, F Huang, J Zhou Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 335 | 2024 |
| mPLUG-2: A modularized multi-modal foundation model across text, image and video H Xu, Q Ye, M Yan, Y Shi, J Ye, Y Xu, C Li, B Bi, Q Qian, W Wang, G Xu, ... Proceedings of International Conference on Machine Learning, 2023 | 129 | 2023 |
| Ureader: Universal ocr-free visually-situated language understanding with multimodal large language model J Ye, A Hu, H Xu, Q Ye, M Yan, G Xu, C Li, J Tian, Q Qian, J Zhang, Q Jin, ... Association for Computational Linguistics: EMNLP 2023, 2841–2858, 2023 | 113 | 2023 |
| Exploring global diverse attention via pairwise temporal relation for video summarization P Li, Q Ye, L Zhang, L Yuan, X Xu, L Shao Pattern Recognition 111, 107677, 2021 | 111 | 2021 |
| mplug-docowl: Modularized multimodal large language model for document understanding J Ye, A Hu, H Xu, Q Ye, M Yan, Y Dan, C Zhao, G Xu, C Li, J Tian, Q Qi, ... arXiv preprint arXiv:2307.02499, 2023 | 109 | 2023 |
| Evaluation and analysis of hallucination in large vision-language models J Wang, Y Zhou, G Xu, P Shi, C Zhao, H Xu, Q Ye, M Yan, J Zhang, J Zhu, ... arXiv preprint arXiv:2308.15126, 2023 | 106 | 2023 |
| Explainable AI For COVID-19 CT Classifiers: An Initial Comparison Study Q Ye, J Xia, G Yang IEEE International Symposium on Computer-Based Medical Systems (CBMS 2021), 2021 | 95 | 2021 |
| Hitea: Hierarchical temporal-aware video-language pre-training Q Ye, G Xu, M Yan, H Xu, Q Qian, J Zhang, F Huang Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 75 | 2023 |
| Hallucination augmented contrastive learning for multimodal large language model C Jiang, H Xu, M Dong, J Chen, W Ye, M Yan, Q Ye, J Zhang, F Huang, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 70 | 2024 |
| Temporal Cue Guided Video Highlight Detection with Low-Rank Audio-Visual Fusion Q Ye, X Shen, Y Gao, Z Wang, Q Bi, P Li, G Yang International Conference on Computer Vision (ICCV 2021), 2021 | 55 | 2021 |
| All grains, one scheme (AGOS): Learning multigrain instance representation for aerial scene classification Q Bi, B Zhou, K Qin, Q Ye, GS Xia IEEE Transactions on Geoscience and Remote Sensing 60, 1-17, 2022 | 40 | 2022 |
| Robust Weakly Supervised Learning for COVID-19 Recognition Using Multi-Center CT Images Q Ye, Y Gao, W Ding, Z Niu, C Wang, Y Jiang, M Wang, EF Fang, ... Applied Soft Computing, 2021 | 36 | 2021 |
| mplug-paperowl: Scientific diagram analysis with the multimodal large language model A Hu, Y Shi, H Xu, J Ye, Q Ye, M Yan, C Li, Q Qian, J Zhang, F Huang Proceedings of the 32nd ACM International Conference on Multimedia, 6929-6938, 2024 | 30 | 2024 |
| Systematic and comprehensive automated ventricle segmentation on ventricle images of the elderly patients: a retrospective study X Zhou, Q Ye, Y Jiang, M Wang, Z Niu, W Menpes-Smith, EF Fang, Z Liu, ... Frontiers in Aging Neuroscience 12, 618538, 2020 | 28 | 2020 |
| Youku-mplug: A 10 million large-scale chinese video-language dataset for pre-training and benchmarks H Xu, Q Ye, X Wu, M Yan, Y Miao, J Ye, G Xu, A Hu, Y Shi, G Xu, C Li, ... arXiv preprint arXiv:2306.04362, 2023 | 22 | 2023 |
| Llava-critic: Learning to evaluate multimodal models T Xiong, X Wang, D Guo, Q Ye, H Fan, Q Gu, H Huang, C Li arXiv preprint arXiv:2410.02712, 2024 | 18 | 2024 |
| Transforming visual scene graphs to image captions X Yang, J Peng, Z Wang, H Xu, Q Ye, C Li, S Huang, F Huang, Z Li, ... arXiv preprint arXiv:2305.02177, 2023 | 18 | 2023 |
| Can clinical symptoms and laboratory results predict CT abnormality? initial findings using novel machine learning techniques in children with COVID-19 infections H Ma, Q Ye, W Ding, Y Jiang, M Wang, Z Niu, X Zhou, Y Gao, C Wang, ... Frontiers in Medicine 8, 699984, 2021 | 18 | 2021 |
Haiyang XuAlibaba Group, DIDI AI LABS, SEUDirección de correo verificada de seu.edu.cn
