no code implementations • 26 Apr 2023 • Yan Wang, Jian Cheng, Yixin Chen, Shuai Shao, Lanyun Zhu, Zhenzhou Wu, Tao Liu, Haogang Zhu
In FVP, the visual prompt is parameterized using only a small amount of low-frequency learnable parameters in the input frequency space, and is learned by minimizing the segmentation loss between the predicted segmentation of the prompted target image and reliable pseudo segmentation label of the target image under the frozen model.
no code implementations • 3 Feb 2022 • Tao Liu, Shu Guo, Hao liu, Rui Kang, Mingyang Bai, Jiyang Jiang, Wei Wen, Xing Pan, Jun Tai, JianXin Li, Jian Cheng, Jing Jing, Zhenzhou Wu, Haijun Niu, Haogang Zhu, Zixiao Li, Yongjun Wang, Henry Brodaty, Perminder Sachdev, Daqing Li
Degeneration and adaptation are two competing sides of the same coin called resilience in the progressive processes of brain aging or diseases.
1 code implementation • 6 Jun 2021 • Jian Cheng, Ziyang Liu, Hao Guan, Zhenzhou Wu, Haogang Zhu, Jiyang Jiang, Wei Wen, DaCheng Tao, Tao Liu
In this paper, a novel 3D convolutional network, called two-stage-age-network (TSAN), is proposed to estimate brain age from T1-weighted MRI data.
no code implementations • 10 Feb 2020 • Yingdong Hu, Liang Zhang, Wei Shan, Xiaoxiao Qin, Jing Qi, Zhenzhou Wu, Yang Yuan
In the big data era, many organizations face the dilemma of data sharing.
no code implementations • 5 Oct 2018 • Evelyn Chee, Zhenzhou Wu
Our proposed method was evaluated on magnetic resonance images of the axial view of human brain and compared with the performance of a conventional image registration method.
no code implementations • 31 May 2017 • Zhenzhou Wu, Sean Saito
Traditionally, classifying large hierarchical labels with more than 10000 distinct traces can only be achieved with flatten labels.
no code implementations • 29 May 2017 • Zhenzhou Wu, Xin Zheng, Daniel Dahlmeier
Despite the success of deep learning on many fronts especially image and speech, its application in text classification often is still not as good as a simple linear SVM on n-gram TF-IDF representation especially for smaller datasets.
no code implementations • 15 Jun 2016 • Zhenzhou Wu, Sunil Sivadas, Yong Kiam Tan, Ma Bin, Rick Siow Mong Goh
Enhancement is achieved by learning a nonlinear mapping function from the features of the corrupted speech signal to that of the reference clean speech signal.
no code implementations • 17 Jun 2015 • Zhenzhou Wu, Shinji Takaki, Junichi Yamagishi
This paper proposes a deep denoising auto-encoder technique to extract better acoustic features for speech synthesis.