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Ziyang Xu (徐子扬) I am currently pursuing my third-year Master's degree within the School of Electronic Information and Communications(EIC) at Huazhong University of Science and Technology(HUST), benefitting from the guidance of Professors Xinggang Wang and Wenyu Liu. Prior to this, I obtained a B.E. degree in Information Engineering from School of Information Engineering(IE), Wuhan University of Technology(WHUT) in 2022. |
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ResearchMy scholarly interests revolve around the fields of Artificial General Intelligence, Computer Vision, Image/Video Generation, Medical Image Analysis, and Object Detection/Segmentation. Representative papers are highlighted. |
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XS-VID: An Extremely Small Video Object Detection Dataset
Jiahao Guo, Ziyang Xu, Lianjun Wu, Fei Gao, Wenyu Liu, Xinggang Wang Conference on Neural Information Processing Systems (NIPS), 2024, [Under Review] Paper / Code(YOLOFT) / Dataset(XS-VID) XS-VID dataset comprises aerial data from various periods and scenes, and extensively collects three types of objects with smaller pixel areas: extremely small (0-12^2), relatively small (12^2-20^2), and generally small (20^2-32^2). XS-VID offers unprecedented breadth and depth in covering and quantifying minuscule objects, significantly enriching the scene and object diversity in the dataset. YOLOFT enhances local feature associations and integrates temporal motion features, significantly improving the accuracy and stability of Small Video Object Detection. |
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Large-scale Pretrained Frame Generative Model Enables Real-Time Low-Dose DSA Imaging: an AI System Development and Multicenter Validation Study
Huangxuan Zhao*, Ziyang Xu*, Linxia Wu*, Lei Chen*, Ziwei Cui, Jinqiang Ma, Tao Sun, Yu Lei, Nan Wang, Hongyao Hu, Yiqing Tan, Wei Lu, Wenzhong Yang, Kaibing Liao, Gaojun Teng, Xiaoyun Liang, Yi Li, Congcong Feng, Xiaoyu Han, P.Matthijs van der Sluij, Charles B.L.M. Majoie, Wim H. van Zwam, Yun Feng, Theo van Walsum, Aad van der Lugt, Wenyu Liu, Xuefeng Kan, Ruisheng Su, Weihua Zhang, Xinggang Wang, Chuansheng Zheng * equal contribution Med (Cell Press), 2024 Paper / Code GenDSA is a large-scale pretrained multi-frame generative model-based real-time and low-dose DSA imaging system, which pre-trained, fine-tuned and tested on ten million of images from 35 hospitals. Suitable for most DSA scanning protocols, GenDSA could reduce the DSA frame rate (i.e., radiation dose) to 1/3 and generates video that was virtually identical to clinically available protocols. Videos generated by GenDSA reach a comparable level to the full-sampled videos, both in terms of overall quality (4.905 vs 4.935) and lesion assessment (4.825 vs 4.860), which fully demonstrated the potential of GenDSA for clinical applications. |
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MoSt-DSA: Modeling Motion and Structural Interactions for Direct Multi-Frame Interpolation in DSA Images
Ziyang Xu, Huangxuan Zhao, Ziwei Cui, Wenyu Liu, Chuansheng Zheng, Xinggang Wang European Conference on Artificial Intelligence (ECAI), 2024 Paper / Code MoSt-DSA is the first work that uses deep learning for DSA frame interpolation, comprehensively achieving SOTA in accuracy, speed, visual effect, and memory usage. Meanwhile, MoSt-DSA is also the first method that directly achieves any number of interpolations at any time steps with just one forward pass during both training and testing. If applied clinically, MoSt-DSA can significantly reduce the DSA radiation dose received by doctors and patients when applied clinically, lowering it by 50%, 67%, and 75% when interpolating 1 to 3 frames, respectively. |