World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
54
Citations
11357
World Ranking
4583
National Ranking
614

Overview

Xi Li is a researcher affiliated with Zhejiang University in China. Their research spans multiple areas within computer science and engineering, with a strong focus on computer vision, artificial intelligence, and related subfields.

The scientist has contributed extensively to topics including:

  • Advanced Neural Network Applications
  • Advanced Image and Video Retrieval Techniques
  • Domain Adaptation and Few-Shot Learning
  • Multimodal Machine Learning Applications
  • Video Surveillance and Tracking Methods
  • Anomaly Detection Techniques and Applications
  • Human Pose and Action Recognition

Li's research has been published primarily in venues such as:

  • arXiv (Cornell University)
  • IEEE Transactions on Image Processing
  • IEEE Transactions on Neural Networks and Learning Systems
  • Neurocomputing
  • IEEE Transactions on Pattern Analysis and Machine Intelligence

Some recent papers include:

  • "Ultra Fast Deep Lane Detection With Hybrid Anchor Driven Ordinal Classification," 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • "A lightweight neural network with strong robustness for bearing fault diagnosis," 2020, Measurement
  • "A Review on Methods and Applications in Multimodal Deep Learning," 2022, ACM Transactions on Multimedia Computing Communications and Applications
  • "Deep Attentive Video Summarization With Distribution Consistency Learning," 2020, IEEE Transactions on Neural Networks and Learning Systems
  • "Memory-Efficient Class-Incremental Learning for Image Classification," 2021, IEEE Transactions on Neural Networks and Learning Systems

Frequent coauthors collaborating with Xi Li include:

  • Songyuan Li
  • Guangcong Zheng
  • Zequn Qin
  • Hanbin Zhao
  • Pengyi Zhang

Li has published mainly within the fields of:

  • Computer Science
  • Engineering

Their work involves advanced methodologies in computer vision and recognition, neural network robustness, and multimodal data integration, contributing to ongoing developments in machine learning applications across different domains.

Best Publications

  • A survey of appearance models in visual object tracking

    Xi Li;Weiming Hu;Chunhua Shen;Zhongfei Zhang

  • Deeply-Learned Part-Aligned Representations for Person Re-identification

    Liming Zhao;Xi Li;Yueting Zhuang;Jingdong Wang

  • DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection

    Xi Li;Liming Zhao;Lina Wei;Ming-Hsuan Yang

  • Ultra Fast Structure-aware Deep Lane Detection

    Zequn Qin;Huanyu Wang;Xi Li

  • A Survey on Generative Adversarial Networks: Variants, Applications, and Training

    Abdul Jabbar;Xi Li;Bourahla Omar

  • Adaptive Graph Representation Learning for Video Person Re-Identification

    Yiming Wu;Omar El Farouk Bourahla;Xi Li;Fei Wu

  • Towards a new generation of artificial intelligence in China

    Fei Wu;Cewu Lu;Mingjie Zhu;Hao Chen

  • Contextual Hypergraph Modeling for Salient Object Detection

    Xi Li;Yao Li;Chunhua Shen;Anthony Dick

  • Deep Q Learning Driven CT Pancreas Segmentation With Geometry-Aware U-Net

    Yunze Man;Yangsibo Huang;Junyi Feng;Xi Li

  • Spatio-Temporal Graph Routing for Skeleton-Based Action Recognition

    Bin Li;Xi Li;Zhongfei Zhang;Fei Wu

  • Deeply-Learned Part-Aligned Representations for Person Re-Identification

    Liming Zhao;Xi Li;Jingdong Wang;Yueting Zhuang

  • Sequential particle swarm optimization for visual tracking

    Xiaoqin Zhang;Weiming Hu;S. Maybank;Xi Li

  • Deep Air Learning: Interpolation, Prediction, and Feature Analysis of Fine-grained Air Quality

    Zhongang Qi;Tianchun Wang;Guojie Song;Weisong Hu

  • Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion

    Peng Sun;Wenhu Zhang;Huanyu Wang;Songyuan Li

  • Single and Multiple Object Tracking Using Log-Euclidean Riemannian Subspace and Block-Division Appearance Model

    Weiming Hu;Xi Li;Wenhan Luo;Xiaoqin Zhang

  • Incremental Tensor Subspace Learning and Its Applications to Foreground Segmentation and Tracking

    Weiming Hu;Xi Li;Xiaoqin Zhang;Xinchu Shi

  • Robust Visual Tracking Based on Incremental Tensor Subspace Learning

    Xi Li;Weiming Hu;Zhongfei Zhang;Xiaoqin Zhang

  • Visual tracking via incremental Log-Euclidean Riemannian subspace learning

    Xi Li;Weiming Hu;Zhongfei Zhang;Xiaoqin Zhang

  • Deep Air Learning: Interpolation, Prediction, and Feature Analysis of Fine-Grained Air Quality

    Zhongang Qi;Tianchun Wang;Guojie Song;Weisong Hu

  • Learning Hash Functions Using Column Generation

    Xi Li;Guosheng Lin;Chunhua Shen;Anton Van den Hengel

  • Body Structure Aware Deep Crowd Counting

    Siyu Huang;Xi Li;Zhongfei Zhang;Fei Wu

  • An Incremental DPMM-Based Method for Trajectory Clustering, Modeling, and Retrieval

    Weiming Hu;Xi Li;Guodong Tian;S. Maybank

Frequent Co-Authors

Fei Wu
Fei Wu Zhejiang University
Zhongfei Zhang
Zhongfei Zhang Binghamton University
Weiming Hu
Weiming Hu Chinese Academy of Sciences
Yueting Zhuang
Yueting Zhuang Zhejiang University
Anthony Dick
Anthony Dick University of Adelaide
Chunhua Shen
Chunhua Shen Zhejiang University
Anton van den Hengel
Anton van den Hengel University of Adelaide
Haibin Ling
Haibin Ling Westlake University
Stephen J. Maybank
Stephen J. Maybank Birkbeck, University of London
Jingdong Wang
Jingdong Wang Baidu (China)

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring online education in Computer Science or related fields opens up various opportunities—even if your academic record isn’t perfect. Several online colleges that accept 2.0 gpa make it possible for students with lower GPAs to pursue a degree and advance their knowledge without traditional barriers.

For those eager to enter the workforce quickly, an accelerated computer science degree online can help you earn credentials faster, giving you a head start in the job market. These programs are designed for motivated learners ready to jump straight into tech roles or further study.

Career paths aren’t limited to computer science. You might wonder, what can you do with an environmental science degree? Options include environmental consulting, research, policy, and sustainability roles—industries in high demand as society prioritizes green solutions.

Likewise, an environmental engineering degree online opens doors to rewarding careers blending technology and the environment. With flexibility in online learning, these pathways make STEM and tech careers more accessible than ever before.

Best Scientists Citing Xi Li

Trending Scientists

Recently Published Articles