World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
45
Citations
9843
World Ranking
7126
National Ranking
226

Overview

Lingqiao Liu is affiliated with the University of Adelaide in Australia and contributes extensively to the field of computer science, focusing particularly on artificial intelligence and computer vision. Their research spans several subfields including radiology, nuclear medicine and imaging, media technology, and biomedical engineering.

The scientist's work covers a range of main research topics such as domain adaptation and few-shot learning, topic modeling, multimodal machine learning applications, advanced neural network applications, video surveillance and tracking methods, natural language processing techniques, and advanced image and video retrieval techniques.

Frequent co-authors of Lingqiao Liu include:

  • Luping Zhou
  • Yinjie Lei
  • Peng Wang
  • Yanning Zhang
  • Zhanyu Wang

Lingqiao Liu has published primarily in venues known for computer vision and neural networks, with notable frequent publication venues including:

  • arXiv (Cornell University)
  • Pattern Recognition
  • IEEE Transactions on Neural Networks and Learning Systems
  • Meta-Radiology
  • International Journal of Computer Vision

Among recent papers authored or co-authored by Lingqiao Liu are:

  • Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Feature Encoding With Autoencoders for Weakly Supervised Anomaly Detection, 2021, IEEE Transactions on Neural Networks and Learning Systems
  • A Biomimetic Polymer Magnetic Nanocarrier Polarizing Tumor-Associated Macrophages for Potentiating Immunotherapy, 2020, Small
  • Towards using count-level weak supervision for crowd counting, 2020, Pattern Recognition
  • R2GenGPT: Radiology Report Generation with frozen LLMs, 2023, Meta-Radiology

Best Publications

  • Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection

    Dong Gong;Lingqiao Liu;Vuong Le;Budhaditya Saha

  • In defense of soft-assignment coding

    Lingqiao Liu;Lei Wang;Xinwang Liu

  • What Value Do Explicit High Level Concepts Have in Vision to Language Problems

    Qi Wu;Chunhua Shen;Lingqiao Liu;Anthony Dick

  • From Motion Blur to Motion Flow: A Deep Learning Solution for Removing Heterogeneous Motion Blur

    Dong Gong;Jie Yang;Lingqiao Liu;Yanning Zhang

  • Graph-Structured Representations for Visual Question Answering

    Damien Teney;Lingqiao Liu;Anton van den Hengel

  • Adversarial PoseNet: A Structure-Aware Convolutional Network for Human Pose Estimation

    Yu Chen;Chunhua Shen;Xiu-Shen Wei;Lingqiao Liu

  • Deep learning features at scale for visual place recognition

    Zetao Chen;Adam Jacobson;Niko Sunderhauf;Ben Upcroft

  • Towards Effective Low-Bitwidth Convolutional Neural Networks

    Bohan Zhuang;Chunhua Shen;Mingkui Tan;Lingqiao Liu

  • The treasure beneath convolutional layers: Cross-convolutional-layer pooling for image classification

    Lingqiao Liu;Chunhua Shen;Anton van den Hengel

  • ZegCLIP: Towards Adapting CLIP for Zero-shot Semantic Segmentation

    Unknown

  • Less is More: Zero-Shot Learning from Online Textual Documents with Noise Suppression

    Ruizhi Qiao;Lingqiao Liu;Chunhua Shen;Anton van den Hengel

  • Seeing Deeply and Bidirectionally: A Deep Learning Approach for Single Image Reflection Removal

    Jie Yang;Dong Gong;Lingqiao Liu;Qinfeng Shi

  • Towards Context-Aware Interaction Recognition for Visual Relationship Detection

    Bohan Zhuang;Lingqiao Liu;Chunhua Shen;Ian Reid

  • Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation

    Bohan Zhuang;Chunhua Shen;Mingkui Tan;Lingqiao Liu

  • Temporal Pyramid Pooling-Based Convolutional Neural Network for Action Recognition

    Peng Wang;Yuanzhouhan Cao;Chunhua Shen;Lingqiao Liu

  • Piecewise Classifier Mappings: Learning Fine-Grained Learners for Novel Categories With Few Examples

    Xiu-Shen Wei;Peng Wang;Lingqiao Liu;Chunhua Shen

  • Feature Encoding With Autoencoders for Weakly Supervised Anomaly Detection.

    Yingjie Zhou;Xucheng Song;Yanru Zhang;Fanxing Liu

  • Mid-level deep pattern mining

    Yao Li;Lingqiao Liu;Chunhua Shen;Anton van den Hengel

  • METransformer: Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens

    Unknown

  • RPC: A Large-Scale Retail Product Checkout Dataset.

    Xiu-Shen Wei;Quan Cui;Lei Yang;Peng Wang

  • Learning Context Flexible Attention Model for Long-Term Visual Place Recognition

    Zetao Chen;Lingqiao Liu;Inkyu Sa;Zongyuan Ge

  • Multi-attention Network for One Shot Learning

    Peng Wang;Lingqiao Liu;Chunhua Shen;Zi Huang

  • Towards Context-aware Interaction Recognition

    Bohan Zhuang;Lingqiao Liu;Chunhua Shen;Ian D. Reid

Frequent Co-Authors

Chunhua Shen
Chunhua Shen Zhejiang University
Anton van den Hengel
Anton van den Hengel University of Adelaide
Ian Reid
Ian Reid University of Adelaide
Mingkui Tan
Mingkui Tan South China University of Technology
Heng Tao Shen
Heng Tao Shen University of Electronic Science and Technology of China
Xiu-Shen Wei
Xiu-Shen Wei Southeast University
Qinfeng Shi
Qinfeng Shi University of Adelaide
Guosheng Lin
Guosheng Lin Nanyang Technological University
Lei Zhang
Lei Zhang Hong Kong Polytechnic University
Qi Wu
Qi Wu University of Adelaide

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