Beijing, China

Yisu Zhou



Average Co-Inventor Count = 3.0

ph-index = 1

Forward Citations = 11(Granted Patents)


Company Filing History:


Years Active: 2016

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1 patent (USPTO):Explore Patents

Title: Yisu Zhou: Innovator in Facial Recognition Technology

Introduction

Yisu Zhou is a prominent inventor based in Beijing, China. He has made significant contributions to the field of facial recognition technology. His innovative work focuses on enhancing image parsing through advanced neural network techniques.

Latest Patents

Yisu Zhou holds a patent for a "Hierarchical interlinked multi-scale convolutional network for image parsing." This disclosed facial recognition system includes face parsing capabilities. In one approach, the face parsing is based on a hierarchical interlinked multiscale convolutional neural network (HIM) to identify locations and/or footprints of components of a face image. The HIM generates multiple levels of image patches from different resolution images of the face image, where image patches for different levels have different resolutions. Moreover, the HIM integrates the image patches for different levels to generate interlinked image patches for different levels, where interlinked image patches for different levels have different resolutions. Furthermore, the HIM combines the interlinked image patches to identify refined locations and/or footprints of components. Yisu Zhou has 1 patent to his name.

Career Highlights

Yisu Zhou is currently employed at Beijing Kuangshi Technology Co., Ltd. His work at this company has allowed him to push the boundaries of facial recognition technology. He has been instrumental in developing systems that improve the accuracy and efficiency of image parsing.

Collaborations

Yisu Zhou collaborates with talented individuals in his field, including Qi Yin and Zhimin Cao. Their combined expertise contributes to the advancement of innovative technologies in facial recognition.

Conclusion

Yisu Zhou is a key figure in the development of facial recognition systems, with a focus on hierarchical interlinked multi-scale convolutional networks. His contributions are paving the way for future advancements in image parsing technology.

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