World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
61
Citations
19964
World Ranking
3012
National Ranking
407

Overview

Shenghua Gao is affiliated with ShanghaiTech University in China, with a research focus primarily in the field of Computer Science. Their work spans several subfields including Computer Vision and Pattern Recognition, Artificial Intelligence, Computational Mechanics, Computer Graphics and Computer-Aided Design, and Molecular Biology.

The scientist has contributed substantially to topics such as Human Pose and Action Recognition, Advanced Vision and Imaging, Anomaly Detection Techniques and Applications, Video Surveillance and Tracking Methods, 3D Shape Modeling and Analysis, Domain Adaptation and Few-Shot Learning, and Advanced Neural Network Applications.

Shenghua Gao's recent papers include:

  • "Appearance-Motion Memory Consistency Network for Video Anomaly Detection," 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • "DeepPROTACs is a deep learning-based targeted degradation predictor for PROTACs," 2022, Nature Communications
  • "Future Frame Prediction Network for Video Anomaly Detection," 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • "AS-MLP: An Axial Shifted MLP Architecture for Vision," 2021, arXiv (Cornell University)
  • "Normal graph: Spatial temporal graph convolutional networks based prediction network for skeleton based video anomaly detection," 2020, Neurocomputing

The frequent co-authors who have collaborated with Shenghua Gao include Weixin Luo, Dongze Lian, Wen Liu, Yanyu Xu, and Kang Zhou.

Regarding publication venues, Shenghua Gao has frequently published in arXiv (Cornell University), IEEE Transactions on Pattern Analysis and Machine Intelligence, Proceedings of the AAAI Conference on Artificial Intelligence, Neurocomputing, and the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Best Publications

  • CE-Net: Context Encoder Network for 2D Medical Image Segmentation

    Zaiwang Gu;Jun Cheng;Huazhu Fu;Kang Zhou

  • PCANet: A Simple Deep Learning Baseline for Image Classification?

    Tsung-Han Chan;Kui Jia;Shenghua Gao;Jiwen Lu

  • Single-Image Crowd Counting via Multi-Column Convolutional Neural Network

    Yingying Zhang;Desen Zhou;Siqin Chen;Shenghua Gao

  • Future Frame Prediction for Anomaly Detection - A New Baseline

    Wen Liu;Weixin Luo;Dongze Lian;Shenghua Gao

  • A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN Framework

    Weixin Luo;Wen Liu;Shenghua Gao

  • Local features are not lonely – Laplacian sparse coding for image classification

    Shenghua Gao;Ivor Wai-Hung Tsang;Liang-Tien Chia;Peilin Zhao

  • Remembering history with convolutional LSTM for anomaly detection

    Weixin Luo;Wen Liu;Shenghua Gao

  • Kernel sparse representation for image classification and face recognition

    Shenghua Gao;Ivor Wai-Hung Tsang;Liang-Tien Chia

  • Laplacian Sparse Coding, Hypergraph Laplacian Sparse Coding, and Applications

    Shenghua Gao;Ivor Wai-Hung Tsang;Liang-Tien Chia

  • Region-Based Saliency Detection and Its Application in Object Recognition

    Zhixiang Ren;Shenghua Gao;Liang-Tien Chia;Ivor Wai-Hung Tsang

  • Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesis

    Wen Liu;Zhixin Piao;Jie Min;Wenhan Luo

  • Encoding Crowd Interaction with Deep Neural Network for Pedestrian Trajectory Prediction

    Yanyu Xu;Zhixin Piao;Shenghua Gao

  • Gaze Prediction in Dynamic 360° Immersive Videos

    Yanyu Xu;Yanbing Dong;Junru Wu;Zhengzhong Sun

  • Face Aging with Identity-Preserved Conditional Generative Adversarial Networks

    Xu Tang;Zongwei Wang;Weixin Luo;Shenghua Gao

  • Single Sample Face Recognition via Learning Deep Supervised Autoencoders

    S. Gao;Y. Zhang;K. Jia;J. Lu

  • Sparse Representation With Kernels

    Shenghua Gao;I. W. Tsang;Liang-Tien Chia

  • Structured3D: A Large Photo-Realistic Dataset for Structured 3D Modeling

    Jia Zheng;Junfei Zhang;Jing Li;Rui Tang

  • Fast-MVSNet: Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton Refinement

    Zehao Yu;Shenghua Gao

  • Video Anomaly Detection with Sparse Coding Inspired Deep Neural Networks

    Weixin Luo;Wen Liu;Dongze Lian;Jinhui Tang

  • Learning to Parse Wireframes in Images of Man-Made Environments

    Kun Huang;Yifan Wang;Zihan Zhou;Tianjiao Ding

  • Learning Category-Specific Dictionary and Shared Dictionary for Fine-Grained Image Categorization

    Shenghua Gao;Ivor Wai-Hung Tsang;Yi Ma

  • Density Map Regression Guided Detection Network for RGB-D Crowd Counting and Localization

    Dongze Lian;Jing Li;Jia Zheng;Weixin Luo

Frequent Co-Authors

Jiang Liu
Jiang Liu Southern University of Science and Technology
Jun Cheng
Jun Cheng University of Chinese Academy of Sciences
Yi Ma
Yi Ma University of Hong Kong
Jingyi Yu
Jingyi Yu ShanghaiTech University
Ivor W. Tsang
Ivor W. Tsang Agency for Science, Technology and Research
Yitian Zhao
Yitian Zhao Chinese Academy of Sciences
Huazhu Fu
Huazhu Fu Agency for Science, Technology and Research
Kui Jia
Kui Jia South China University of Technology
Jinhui Tang
Jinhui Tang Nanjing University of Science and Technology
Lixin Duan
Lixin Duan University of Electronic Science and Technology of China

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