Search Results for author: Jinzhuo Wang

Found 7 papers, 1 papers with code

Automated Movement Detection with Dirichlet Process Mixture Models and Electromyography

no code implementations15 Feb 2023 Navin Cooray, Zhenglin Li, Jinzhuo Wang, Christine Lo, Mahnaz Arvaneh, Mkael Symmonds, Michele Hu, Maarten De Vos, Lyudmila S Mihaylova

This study proposes a framework for automated limb-movement detection by fusing data from two EMG sensors (from the left and right limb) through a Dirichlet process mixture model.

Decision Making Specificity

More Information Supervised Probabilistic Deep Face Embedding Learning

no code implementations ICML 2020 Ying Huang, Shangfeng Qiu, Wenwei Zhang, Xianghui Luo, Jinzhuo Wang

Researches using margin based comparison loss demonstrate the effectiveness of penalizing the distance between face feature and their corresponding class centers.

Face Recognition Open Set Learning

Deep Frequent Spatial Temporal Learning for Face Anti-Spoofing

no code implementations20 Jan 2020 Ying Huang, Wenwei Zhang, Jinzhuo Wang

Face anti-spoofing is crucial for the security of face recognition system, by avoiding invaded with presentation attack.

Face Anti-Spoofing Face Recognition

Video Imagination from a Single Image with Transformation Generation

1 code implementation13 Jun 2017 Baoyang Chen, Wenmin Wang, Jinzhuo Wang, Xiongtao Chen

To overcome those problems, we propose a new framework that produce imaginary videos by transformation generation.

Image Quality Assessment

Long-Term Video Interpolation with Bidirectional Predictive Network

no code implementations13 Jun 2017 Xiongtao Chen, Wenmin Wang, Jinzhuo Wang, Weimian Li, Baoyang Chen

In this paper, we present a novel deep architecture called bidirectional predictive network (BiPN) that predicts intermediate frames from two opposite directions.

Beyond Monte Carlo Tree Search: Playing Go with Deep Alternative Neural Network and Long-Term Evaluation

no code implementations13 Jun 2017 Jinzhuo Wang, Wenmin Wang, Ronggang Wang, Wen Gao

We show such setting can preserve more contexts of local features and its evolutions which are beneficial for move prediction.

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