1 code implementation • 2 Apr 2024 • Xu He, Qiaochu Huang, Zhensong Zhang, Zhiwei Lin, Zhiyong Wu, Sicheng Yang, Minglei Li, Zhiyi Chen, Songcen Xu, Xiaofei Wu
While previous works mostly generate structural human skeletons, resulting in the omission of appearance information, we focus on the direct generation of audio-driven co-speech gesture videos in this work.
no code implementations • 18 Sep 2023 • Zhiyi Chen, Harshal Maske, Huanyi Shui, Devesh Upadhyay, Michael Hopka, Joseph Cohen, Xingjian Lai, Xun Huan, Jun Ni
This study introduces a stochastic deep Koopman (SDK) framework to model the complex behavior of MMSs.
1 code implementation • 20 Aug 2023 • Peng Li, Zhiyi Chen, Xu Chu, Kexin Rong
Data preprocessing is a crucial step in the machine learning process that transforms raw data into a more usable format for downstream ML models.
no code implementations • 21 Aug 2021 • Zhiyi Chen, Tong Lin
In Principal Gradient Direction, we optimize a target gradient that not only represents the major contribution of past gradients, but also retains the new knowledge of the current gradient.
no code implementations • 10 Oct 2020 • Haoming Li, Xin Yang, Jiamin Liang, Wenlong Shi, Chaoyu Chen, Haoran Dou, Rui Li, Rui Gao, Guangquan Zhou, Jinghui Fang, Xiaowen Liang, Ruobing Huang, Alejandro Frangi, Zhiyi Chen, Dong Ni
However, the lack of sharp boundaries in US images still remains an inherent challenge for segmentation.
no code implementations • 1 Apr 2020 • Jiamin Liang, Xin Yang, Haoming Li, Yi Wang, Manh The Van, Haoran Dou, Chaoyu Chen, Jinghui Fang, Xiaowen Liang, Zixin Mai, Guowen Zhu, Zhiyi Chen, Dong Ni
Efficiently synthesizing realistic, editable and high resolution US images can solve the problems.
no code implementations • 25 Sep 2019 • Zhiyi Chen, Tong Lin*
Gradient Episodic Memory (GEM) is an effective model for continual learning, where each gradient update for the current task is formulated as a quadratic program problem with inequality constraints that alleviate catastrophic forgetting of previous tasks.
no code implementations • 16 Oct 2018 • Guofu Li, Pengjia Zhu, Jin Li, Zhemin Yang, Ning Cao, Zhiyi Chen
Adversarial machine learning is a fast growing research area, which considers the scenarios when machine learning systems may face potential adversarial attackers, who intentionally synthesize input data to make a well-trained model to make mistake.