World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
43
Citations
8965
World Ranking
7904
National Ranking
1038

Overview

Wen Li is a researcher affiliated with the University of Electronic Science and Technology of China. Their work primarily spans the domain of computer science, with a concentration on computer vision and pattern recognition, artificial intelligence, and computational mechanics. Their research also touches on molecular biology and accounting through interdisciplinary applications.

Their publication record includes contributions to several prominent venues. Frequent publication outlets include arXiv (Cornell University), the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Transactions on Circuits and Systems for Video Technology, International Journal of Computer Vision, and IEEE Transactions on Multimedia.

Wen Li's recent notable papers include:

  • Insights into the post-translational modification and its emerging role in shaping the tumor microenvironment, 2021, Signal Transduction and Targeted Therapy
  • Tensorized Multi-view Subspace Representation Learning, 2020, International Journal of Computer Vision
  • Scale-Aware Domain Adaptive Faster R-CNN, 2021, International Journal of Computer Vision
  • Revisiting Random Channel Pruning for Neural Network Compression, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • A novel progressively undersampling method based on the density peaks sequence for imbalanced data, 2020, Knowledge-Based Systems

The fields of study central to Wen Li's research are reinforced by their work in various specialized topics. Notable main topics of their work include:

  • Domain Adaptation and Few-Shot Learning
  • Advanced Neural Network Applications
  • Multimodal Machine Learning Applications
  • Human Pose and Action Recognition
  • Video Surveillance and Tracking Methods
  • Generative Adversarial Networks and Image Synthesis
  • Sparse and Compressive Sensing Techniques

Collaborations form an important part of Wen Li's research network. Frequent co-authors include:

  • Lixin Duan
  • Jinhong Deng
  • Tiezheng Ge
  • Luc Van Gool
  • Dong Xu

Wen Li's academic contributions are concentrated in computer science disciplines with a strong emphasis on computer vision and artificial intelligence. Their research incorporates both theoretical and applied aspects including image synthesis, neural networks, and domain adaptation techniques. Their published works reflect a recurring engagement with multi-view representation learning, advanced network compression strategies, and data imbalance solutions.

Best Publications

  • Domain Adaptive Faster R-CNN for Object Detection in the Wild

    Yuhua Chen;Wen Li;Christos Sakaridis;Dengxin Dai

  • Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation

    Muhammad Ghifary;W. Bastiaan Kleijn;Mengjie Zhang;David Balduzzi

  • Collaborative and Adversarial Network for Unsupervised Domain Adaptation

    Weichen Zhang;Wanli Ouyang;Wen Li;Dong Xu

  • Learning With Augmented Features for Supervised and Semi-Supervised Heterogeneous Domain Adaptation

    Wen Li;Lixin Duan;Dong Xu;Ivor W. Tsang

  • Appearance-and-Relation Networks for Video Classification

    Limin Wang;Wei Li;Luc Van Gool

  • WebVision Database: Visual Learning and Understanding from Web Data

    Wen Li;Limin Wang;Wei Li;Eirikur Agustsson

  • DLOW: Domain Flow for Adaptation and Generalization

    Rui Gong;Wen Li;Yuhua Chen;Luc Van Gool

  • ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes

    Yuhua Chen;Wen Li;Luc Van Gool

  • Unsupervised Domain Adaptation for Face Anti-Spoofing

    Haoliang Li;Wen Li;Hong Cao;Shiqi Wang

  • Fusing Robust Face Region Descriptors via Multiple Metric Learning for Face Recognition in the Wild

    Zhen Cui;Wen Li;Dong Xu;Shiguang Shan

  • Learning Semantic Segmentation From Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach

    Yuhua Chen;Wen Li;Xiaoran Chen;Luc Van Gool

  • Exploiting Low-Rank Structure from Latent Domains for Domain Generalization

    Zheng Xu;Wen Li;Li Niu;Dong Xu

  • Image Classification by Cross-Media Active Learning With Privileged Information

    Yan Yan;Feiping Nie;Wen Li;Chenqiang Gao

  • Revisiting Random Channel Pruning for Neural Network Compression

    Unknown

  • Text-based image retrieval using progressive multi-instance learning

    Wen Li;Lixin Duan;Dong Xu;Ivor Wai-Hung Tsang

  • Domain Generalization and Adaptation Using Low Rank Exemplar SVMs

    Wen Li;Zheng Xu;Dong Xu;Dengxin Dai

  • Sliced Wasserstein Generative Models

    Jiqing Wu;Zhiwu Huang;Dinesh Acharya;Wen Li

  • Distance Metric Learning Using Privileged Information for Face Verification and Person Re-Identification

    Xinxing Xu;Wen Li;Dong Xu

  • Scale-Aware Domain Adaptive Faster R-CNN

    Yuhua Chen;Haoran Wang;Wen Li;Christos Sakaridis

  • Semi-Supervised Optimal Transport for Heterogeneous Domain Adaptation

    Yuguang Yan;Wen Li;Hanrui Wu;Huaqing Min

  • Improving Web Image Search by Bag-Based Reranking

    Lixin Duan;Wen Li;Ivor Wai-Hung Tsang;Dong Xu

  • Recognizing RGB Images by Learning from RGB-D Data

    Lin Chen;Wen Li;Dong Xu

Frequent Co-Authors

Dong Xu
Dong Xu University of Hong Kong
Luc Van Gool
Luc Van Gool Institute for Computer Science, Artificial Intelligence and Technology (INSAIT)
Tae-Hyun Bae
Tae-Hyun Bae Korea Advanced Institute of Science and Technology
Lixin Duan
Lixin Duan University of Electronic Science and Technology of China
Dengxin Dai
Dengxin Dai Huawei Zurich
Ivor W. Tsang
Ivor W. Tsang Agency for Science, Technology and Research
Wanli Ouyang
Wanli Ouyang Shanghai AI Lab
Kunli Goh
Kunli Goh Nanyang Technological University
Shiguang Shan
Shiguang Shan Chinese Academy of Sciences
Rong Wang
Rong Wang Nanyang Technological University

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