no code implementations • 30 Apr 2024 • Zhendong Liu, Haifeng Xia, Tong Guo, Libo Sun, Ming Shao, Siyu Xia
In addition, a dedicated temporal convolution is applied at each level to learn short-term temporal features, which will be carried over from shallow to deep layers to maximize the leverage of low-level details.
no code implementations • 7 Sep 2023 • Zhendong Liu, Jie Zhang, Qiangqiang He, Chongjun Wang
In the realm of visual recognition, data augmentation stands out as a pivotal technique to amplify model robustness.
no code implementations • 8 Dec 2022 • Zhendong Liu, Wenyu Jiang, Min Guo, Chongjun Wang
Based on the analysis of the internal mechanisms, we develop a mask-based boosting method for data augmentation that comprehensively improves several robustness measures of AI models and beats state-of-the-art data augmentation approaches.
no code implementations • 1 Aug 2021 • Zhendong Liu, Van Manh, Xin Yang, Xiaoqiong Huang, Karim Lekadir, Víctor Campello, Nishant Ravikumar, Alejandro F Frangi, Dong Ni
A style transfer model with style fusion is employed to generate the curriculum samples.
no code implementations • 11 Jan 2021 • Zhendong Liu, Xiaoqiong Huang, Xin Yang, Rui Gao, Rui Li, Yuanji Zhang, Yankai Huang, Guangquan Zhou, Yi Xiong, Alejandro F Frangi, Dong Ni
Deep segmentation models that generalize to images with unknown appearance are important for real-world medical image analysis.
no code implementations • 24 Sep 2020 • Xiaoqiong Huang, Zejian Chen, Xin Yang, Zhendong Liu, Yuxin Zou, Mingyuan Luo, Wufeng Xue, Dong Ni
Based on the zero-shot style transfer to remove appearance shift and test-time augmentation to explore diverse underlying anatomy, our proposed method is effective in combating the appearance shift.
no code implementations • 14 Feb 2020 • Zhendong Liu, Xin Yang, Rui Gao, Shengfeng Liu, Haoran Dou, Shuangchi He, Yuhao Huang, Yankai Huang, Huanjia Luo, Yuanji Zhang, Yi Xiong, Dong Ni
In this paper, we propose a novel and intuitive framework to remove the appearance shift, and hence improve the generalization ability of DNNs.