Tianrui Liu

I am currently an Associate Processor at the Department of Computer Science, National University of Defense Technology (NUDT). Before, I was a Research Associate in the BioMedIA Group in Imperial College London, advised by Professor Daniel Rueckert and Dr. Bernhard Kainz.

I'm interested in computer vision, deep learning, object detection and pattern recognition, image and video processing and medical image analysis.

In my spare time, I like playing piano, reading and playing badminton.

Email  /  CV  /  Google Scholar  /  LinkedIn

• Ph.D. in Computer Vision and Image Processing, Imperial College London
  Nov. 2015 - Nov. 2019
• M.Phil. in Image and Video Processing, University of Hong Kong
  Sep. 2013 - Nov. 2015
• B.Eng. (First Class Honours) in EIE, Hong Kong Polytechnic University
  Sep. 2011 - Jun. 2013
• B.Eng. in Microelectronics, San Yat-Sen University
  Sep. 2009 - Jun. 2011

• Resesarch Associate @ Imperial College London.
  Aug. 2019 - Aug. 2021
• Research Intern @ Tencent AI Lab (Shenzhen, China), advised by Dr. Wenhan Luo
  Oct. 2019 - Apr. 2020
• Visiting Research Scholar @ Hong Kong Polytechnic University, advised by Prof. Kenneth K. M. Lam
  Aug. 2018
• Research Intern @ RISA Sicherheitsanalysen GmbH (Berlin, Germany)
  June. 2018 - Sep. 2018
• Visiting Research Scholar @ Hong Kong Polytechnic University, advised by Prof. Wan-Chi Siu
  Jun. 2016 - Aug. 2016

We propose 3DST-UNet-RL, a deep RL-based framework using a 3D spatio-temporal U-Net for video summarization. We extend our video summarization method for ultrasound scanning videos, where automated report generation with short but relevant video clips is desirable.

In this paper, we propose a gated multi-layer convolutional feature extraction method which can adaptively generate discriminative features for candidate pedestrian regions.

We propose an ultrasound video summarization method to summarize the long examination videos. The proposed method can remove parts that are not relevant for diagnostics and meanwhile guarantees the preservation of decisive diagnostic information.

we propose a novel multi-view motion estimation network (MulViMotion), which integrates 2D cine CMR images acquired in short-axis and long-axis planes to learn a consistent 3D motion field of the heart.

We piloted the end-to-end automation of the mid-trimester screening ultrasound scan using AI enabled tools. Survey responses suggest that the AI toolshelped sonographers to concentrate on image interpretation by removing disruptivetasks.

In this paper, we propose a gated multi-layer convolutional feature extraction method which can adaptively generate discriminative features for candidate pedestrian regions.

We exploits different combination of multi-resolution CNN features for pedestrian candidates of different scales.

Our method extends the Faster R-CNN detection framework by adding a branch of network for semantic image segmentation.

A novel hierarchical random forests based super-resolution (SRHRF) method is proposed to learn statistical priors from external training images.

We propose an effective image labeling method with a hierarchical framework consists of two layers of random forests (RF). In the first layer, RF is performed on superpixel basis. In the second layer, structured RF is applied to make use of the topological distribution of the object classes.

We propose a two-stage framework for fast image interpolation via random forests. The underlying idea of this proposed work is to apply random forests to classify the natural image patch space into numerous subspaces and learn a linear regression model for each subspace to map the low-resolution image patch to high-resolution image patch.

• Chinese Government Award for Outstanding (Non-Government Sponsored) Students Abroad
  2020
• Imperial College Department Scholarship (£130,000), Imperial College London
  2016 - 2019
• Young Scientist Award, IEEE Region 10 Conference TENCON
  2015
• Dean's List of Outstanding Students, HKPolyU
  2012 & 2013
• Outstanding Performance Scholarship (HK$ 80,000), HKSAR Government
  2012
• PolyU EIE (Non-local Student) Scholarship (HK$ 100,000), HKPolyU
  2011

• Convex Optimization
• Machine Learning
• Pratical AI Computing

Journal Reviewer:

• IEEE Transactions on Image Processing (TIP)
• IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
• IEEE Transactions on Medical Imaging (TMI)
• Information Fusion
• Transactions on Geoscience and Remote Sensing (TGRS)
• IEEE Transactions on Multimedia (TMM)
• IEEE Internet of Things Journal (IoT)
• Future Generation Computer Systems
• Pattern Recognation, etc.

• Deep learning in Pedestrian Detection and Video Analysis
  @Huazhong University of Science and Technology, WuHan, China
  Dec. 2020
• Deep Learning for Pedestrian Detection and Medical Video Summarization
  @Nanjing University of Aeronautics and Astronautics, NanJing, China
  Sep. 2020
• What Can Help Pedestrian Detection?
  @National University of Defense Technology, ChangSha, China
  Dec. 2019
• Deep Learning for Pedestrian Detection under Complex Environment
  @NorthEast University，Department of Computer Science, ShenYang, China
  Dec. 2019
• Enhanced Pedestrian Detection using Deep Learning based Semantic Segmentation and Scale-Aware Scheme
  @National Technical University of Athens, Athens, Greece
  Sept. 2018
• Low Resolution Face Detection for Video Surveillance Technologies
  @Artificial Intelligence and Mulitmedia Lab, HK PolyU, HK
  Sept. 2018