Kaichen Zhou
Kaichen Zhou
Department of Computer Science,
Robert Hooke Building
Directions Postal Address
Interests
Robotics, Multimodal Learning, Reinforcement Learning
Biography
I'm a DPhil student (01.2020-) in the Department of Computer Science at the University of Oxford. My Ph.D. work was supervised by Prof. Niki Trigoni and Prof. Andrew Markham, which Lighthouse Scholarship generally funds.
Driven by my interest in Robotics and Computer Vision, my research aims to build an intelligent robotic system that can more efficiently deal with the traditional SLAM problems by combining traditional methods and deep learning. I graduated from the Department of Computing, Imperial College with a First Degree (2019).
Selected Publications
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VMLoc: Variational Fusion For Learning−Based Multimodal Camera Localization
Kaichen Zhou Changhao Chen Bing Wang Muhamad Risqi U. Saputra Niki Trigoni and Andrew Markham
In AAAI Conference on Artificial Intelligence (AAAI). 2021.
Details about VMLoc: Variational Fusion For Learning−Based Multimodal Camera Localization | BibTeX data for VMLoc: Variational Fusion For Learning−Based Multimodal Camera Localization | Download (pdf) of VMLoc: Variational Fusion For Learning−Based Multimodal Camera Localization
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Smart Train Operation Algorithms Based on Expert Knowledge and Reinforcement Learning
Kaichen Zhou‚ Shiji Song‚ Anke Xue‚ Keyou You and Hui Wu.
In IEEE Systems‚ Man‚ and Cybernetics Society. 2020.
Details about Smart Train Operation Algorithms Based on Expert Knowledge and Reinforcement Learning | BibTeX data for Smart Train Operation Algorithms Based on Expert Knowledge and Reinforcement Learning
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Suggestive Annotation of Brain Tumour Images with Gradient−guided Sampling
Chengliang Dai‚ Shuo Wang‚ Yuanhan Mo‚ Kaichen Zhou‚ Elsa Angelini‚ Yike Guo and Wenjia Bai.
In International Conference on Medical Image Computing and Computer Assisted Intervention‚ 2020. 2020.
Details about Suggestive Annotation of Brain Tumour Images with Gradient−guided Sampling | BibTeX data for Suggestive Annotation of Brain Tumour Images with Gradient−guided Sampling