1 code implementation • 24 Nov 2023 • XueWei Li, Zewen Shang, Zhiqiang Liu, Jian Yu, Wei Xiong, Mei Yu
History and future time information includes the trend of airflow changes, whether this dynamic information can be utilized will also affect the prediction effect.
1 code implementation • 1 May 2022 • Tingdi Ren, Haiyong Xu, Gangyi Jiang, Mei Yu, Ting Luo
To address problems, a novel U-Net based Reinforced Swin-Convs Transformer for the Underwater Image Enhancement method (URSCT-UIE) is proposed.
1 code implementation • 22 Aug 2021 • Zhengyong Wang, Liquan Shen, Mei Yu, Kun Wang, Yufei Lin, Mai Xu
However, these methods ignore the significant domain gap between the synthetic and real data (i. e., interdomain gap), and thus the models trained on synthetic data often fail to generalize well to real underwater scenarios.
no code implementations • 20 Aug 2021 • Zhengyong Wang, Liquan Shen, Mei Yu, Yufei Lin, Qiuyu Zhu
The proposed framework includes an analysis network and a synthesis network, one for priors exploration and another for priors integration.
no code implementations • 9 Feb 2021 • Zhiyong Cheng, Jun Deng, Tianyi Wang, Mei Yu
Using the generalized extreme value theory to characterize tail distributions, we address liquidation, leverage, and optimal margins for bitcoin long and short futures positions.
1 code implementation • 13 Oct 2020 • Jianrong Wang, Tong Wu, Shanyu Wang, Mei Yu, Qiang Fang, Ju Zhang, Li Liu
To this end, in this work, we present a novel end-to-end 3D lip motion Network (3LMNet) by utilizing the sentence-level 3D lip motion (S3DLM) to recognize speakers in both the text-independent and text-dependent contexts.
no code implementations • 9 Jul 2020 • Jianrong Wang, Xiaosheng Hu, Li Liu, Wei Liu, Mei Yu, Tianyi Xu
Given a speaker's speech, it is interesting to see if it is possible to generate this speaker's face.
no code implementations • 16 Jul 2018 • Ruiguo Yu, Zhi-Qiang Liu, Xuewei Li, Wenhuan Lu, Mei Yu, Jianrong Wang, Bin Li
There have been a lot of researches based on the time series of the wind power or speed, but In fact, these time series cannot express the temporal and spatial changes of wind, which fundamentally hinders the advance of wind power prediction.