Tianrui Liu

I am now a Lecturer 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

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Education

  • 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

Working Experience / Activities

  • 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

Selected Publications

For a complete list, please check my Google Scholar.

Video Summarization through Reinforcement Learning with a 3D Spatio-Temporal U-Net
Tianrui Liu, Qingjie Meng, Junjie Huang, Athanasios Vlontzos, Daniel Rueckert, Bernhard Kainz
IEEE Transactions on Image Processing, 2022
pdf /

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.

Coupled-Network for Robust Pedestrian Detection with Gated Multi-Layer Feature Extraction and Deformable Occlusion Handling
Tianrui Liu, Wenhan Luo, Lin Ma, Junjie Huang, Tania Stathaki, Tianhong Dai
IEEE Transactions on Image Processing, 2020
pdf / bibtex

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

Ultrasound Video Summarization using Deep Reinforcement Learning
Tianrui Liu, Qingjie Meng, Athanasios Vlontzos, Jeremy Tan, Daniel Rueckert, Bernhard Kainz
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2020
pdf / bibtex

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.

MulViMotion: Shape-aware 3D Myocardial Motion Tracking from Multi-View Cardiac MRI
Qingjie Meng, Chen Qin, Wenjia Bai, Tianrui Liu, Antonio de Marvao, Declan P O’Regan, Daniel Rueckert
Accepted by IEEE Transactions on Medical Imaging, 2022
pdf

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.

Exploring a new paradigm for the fetal anomaly ultrasound scan: Artificial intelligence in real time
Jacqueline Matthew, Emily Skelton, Thomas G. Day, Veronika A. Zimmer, Alberto Gomez, Gavin Wheeler, Nicolas Toussaint, Tianrui Liu, Samuel Budd, Karen Lloyd, Robert Wright, Shujie Deng, Nooshin Ghavami, Matthew Sinclair, Qingjie Meng, Bernhard Kainz, Julia A. Schnabel, Daniel Rueckert, Reza Razavi, John Simpson, Jo Hajnal
Prenatal Diagnosis, 2021
pdf / bibtex

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.

Gated Multi-layer Convolutional Feature Extraction Network for Robust Pedestrian Detection
Tianrui Liu, Jun-Jie Huang, Tianhong Dai, Guangyu Ren, Tania Stathaki
International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020
pdf / bibtex

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

SAM-RCNN: Scale-Aware Multi-Resolution Multi-Channel Pedestrian Detection
Tianrui Liu, Mohamed ElMikaty, Tania Stathaki
British Machine Vision Conference (BMVC), 2019
pdf / bibtex

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

Faster R-CNN for robust pedestrian detection using semantic segmentation network
Tianrui Liu, Tania Stathaki
Frontiers in Neurorobotics, 2018
pdf / bibtex

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

SRHRF+: Self-Example Enhanced Single Image Super-Resolution Using Hierarchical Random Forests
Jun-jie Huang, Tianrui Liu, Pier Luigi Dragotti, Tania Stathaki,
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshop, 2017
pdf / bibtex

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

Hierarchical Semantic Image Labelling method via Random Forests
Tianrui Liu, Shing Chow Chan
IEEE Region 10 Conference TENCON (Young Scientist Award), 2015.
pdf / bibtex

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.

Fast Image Interpolation via Random Forests
Jun-jie Huang, Wan-Chi Siu, Tianrui Liu
IEEE Transactions on Image Processing, 2015.
pdf / bibtex

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.

Selected Awards

  • 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

Professional Activities

    Journal Reviewer:

  • IEEE Transactions on Image Processing (TIP)
  • IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
  • IEEE Transactions on Medical Imaging (TMI)
  • 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.

Talks

  • 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




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