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Computer Science
Australia
2025

D-Index & Metrics

Computer Science

D-Index
67
Citations
38393
World Ranking
2135
National Ranking
67

Research.com Recognitions

  • 2025 - Research.com Computer Science in Australia Leader Award
  • 2023 - Research.com Computer Science in Australia Leader Award
  • 2022 - Research.com Computer Science in Australia Leader Award

Overview

Liang Zheng is affiliated with the Australian National University in Australia and has an extensive publication record in the field of Computer Science. Their research primarily focuses on areas including Computer Vision and Pattern Recognition, Artificial Intelligence, Nuclear and High Energy Physics, Signal Processing, and Civil and Structural Engineering.

The main topics of Liang Zheng's work cover:

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

Frequently collaborating with other researchers, Liang Zheng has coauthored numerous publications alongside colleagues such as Stephen Jay Gould, Yunzhong Hou, Tom Gedeon, Weijian Deng, and Zhongdao Wang.

Their work has appeared in various academic venues, with a significant number published in:

  • arXiv (Cornell University)
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • SSRN Electronic Journal
  • IEEE Transactions on Circuits and Systems for Video Technology
  • IEEE Transactions on Multimedia

Examples of recent papers by Liang Zheng include:

  • Random Erasing Data Augmentation, 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • Learning to Adapt Invariance in Memory for Person Re-identification, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Category-Level Adversarial Adaptation for Semantic Segmentation using Purified Features, 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Circle Loss: A Unified Perspective of Pair Similarity Optimization, 2020, arXiv (Cornell University)
  • Rethinking Triplet Loss for Domain Adaptation, 2020, IEEE Transactions on Circuits and Systems for Video Technology

Liang Zheng's work addresses several domains related to machine learning and computer vision. The emphasis on domain adaptation and neural network optimization techniques reflects ongoing exploration of methods to improve model performance across different datasets and learning scenarios.

Best Publications

  • Scalable Person Re-identification: A Benchmark

    Liang Zheng;Liang Zheng;Liyue Shen;Lu Tian;Shengjin Wang

  • Random Erasing Data Augmentation

    Zhun Zhong;Liang Zheng;Guoliang Kang;Shaozi Li

  • Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline)

    Yifan Sun;Liang Zheng;Yi Yang;Qi Tian

  • Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro

    Zhedong Zheng;Liang Zheng;Yi Yang

  • Re-ranking Person Re-identification with k-Reciprocal Encoding

    Zhun Zhong;Liang Zheng;Donglin Cao;Shaozi Li

  • Person Re-identification: Past, Present and Future

    Liang Zheng;Yi Yang;Alexander G. Hauptmann

  • MARS: A Video Benchmark for Large-Scale Person Re-Identification

    Liang Zheng;Liang Zheng;Zhi Bie;Yifan Sun;Jingdong Wang

  • Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification

    Weijian Deng;Liang Zheng;Qixiang Ye;Guoliang Kang

  • Towards Real-Time Multi-Object Tracking

    Zhongdao Wang;Liang Zheng;Yixuan Liu;Yali Li

  • A Discriminatively Learned CNN Embedding for Person Reidentification

    Zhedong Zheng;Liang Zheng;Yi Yang

  • Circle Loss: A Unified Perspective of Pair Similarity Optimization

    Yifan Sun;Changmao Cheng;Yuhan Zhang;Chi Zhang

  • Improving person re-identification by attribute and identity learning

    Yutian Lin;Liang Zheng;Zhedong Zheng;Yu Wu

  • Person Re-identification in the Wild

    Liang Zheng;Hengheng Zhang;Shaoyan Sun;Manmohan Chandraker

  • Joint Discriminative and Generative Learning for Person Re-Identification

    Zhedong Zheng;Xiaodong Yang;Zhiding Yu;Liang Zheng

  • SVDNet for Pedestrian Retrieval

    Yifan Sun;Liang Zheng;Weijian Deng;Shengjin Wang

  • SIFT Meets CNN: A Decade Survey of Instance Retrieval

    Liang Zheng;Yi Yang;Qi Tian

  • Taking a Closer Look at Domain Shift: Category-Level Adversaries for Semantics Consistent Domain Adaptation

    Yawei Luo;Liang Zheng;Tao Guan;Junqing Yu

  • Unsupervised Person Re-identification: Clustering and Fine-tuning

    Hehe Fan;Liang Zheng;Chenggang Yan;Yi Yang

  • Camera Style Adaptation for Person Re-identification

    Zhun Zhong;Liang Zheng;Zhedong Zheng;Shaozi Li

  • Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-Identification

    Zhun Zhong;Liang Zheng;Zhiming Luo;Shaozi Li

Frequent Co-Authors

Shengjin Wang
Shengjin Wang Tsinghua University
Qi Tian
Qi Tian Huawei Technologies (China)
Shaozi Li
Shaozi Li Xiamen University
Xiaodong Yang
Xiaodong Yang Nvidia (United Kingdom)
Jingdong Wang
Jingdong Wang Baidu (China)
Milind R. Naphade
Milind R. Naphade Nvidia (United States)
Stephen Gould
Stephen Gould Australian National University
Lingxi Xie
Lingxi Xie Huawei Technologies (China)
Mang Ye
Mang Ye Wuhan University
Qixiang Ye
Qixiang Ye Chinese Academy of Sciences

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