Mengyuan Liu
Mengyuan Liu
Assistant Professor, Peking University
Verified email at - Homepage
Cited by
Cited by
Enhanced skeleton visualization for view invariant human action recognition
M Liu, H Liu, C Chen
Pattern Recognition (PR), 2017
Recognizing Human Actions as the Evolution of Pose Estimation Maps
M Liu, J Yuan
IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2018
Skeleton-Based Human Action Recognition Using Spatial Temporal 3D Convolutional Neural Networks
J Tu, M Liu, H Liu
IEEE International conference on multimedia and expo (ICME), 2018
Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose Estimation
W Li, H Liu, R Ding, M Liu, P Wang, W Yang
IEEE Transactions on Multimedia (TMM), 2022
A Survey on 3D Skeleton-Based Action Recognition Using Learning Method
B Ren, M Liu, R Ding, H Liu
Cyborg and Bionic Systems (CBS), 2024
Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-Supervised Action Recognition
T Guo, H Liu, Z Chen, M Liu, T Wang, R Ding
AAAI Conference on Artificial Intelligence (AAAI), 2022
Deformable Pose Traversal Convolution for 3D Action and Gesture Recognition
J Weng, M Liu, X Jiang, J Yuan
European Conference on Computer Vision (ECCV), 2018
3D Action Recognition Using Multi-temporal Depth Motion Maps and Fisher Vector
C Chen, M Liu, B Zhang, J Han, J Jiang, H Liu
International Joint Conference on Artificial Intelligence (IJCAI), 2016
CNN‐Based Reference Comparison Method for Classifying Bare PCB Defects
P Wei, C Liu, M Liu, Y Gao, H Liu
Journal of Engineering (JoE), 2018
PoseFormerV2: Exploring Frequency Domain for Efficient and Robust 3D Human Pose Estimation
Q Zhao, C Zheng, M Liu, P Wang, C Chen
IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2023
Action recognition from depth sequences using weighted fusion of 2D and 3D auto-correlation of gradients features
C Chen, B Zhang, Z Hou, J Jiang, M Liu, Y Yang
Multimedia Tools and Applications (MTA), 2017
Depth Context: A new descriptor for human activity recognition by using sole depth sequences
M Liu, H Liu
Neurocomputing, 2016
Multi-Temporal Depth Motion Maps-Based Local Binary Patterns for 3-D Human Action Recognition
C Chen, M Liu, H Liu, B Zhang, J Han, N Kehtarnavaz
IEEE Access, 2017
Sample Fusion Network: An End-to-End Data Augmentation Network for Skeleton-Based Human Action Recognition
F Meng, H Liu, Y Liang, J Tu, M Liu
IEEE Transactions on Image Processing (TIP), 2019
Consistency-Based Active Learning for Object Detection
W Yu, S Zhu, T Yang, C Chen, M Liu
arXiv:2103.10374, 2021
Spatial-Temporal Data Augmentation Based on LSTM Autoencoder Network for Skeleton-Based Human Action Recognition
J Tu, H Liu, F Meng, M Liu, R Ding
IEEE International Conference on Image Processing (ICIP), 2018
Temporal Decoupling Graph Convolutional Network for Skeleton-Based Gesture Recognition
J Liu, X Wang, C Wang, Y Gao, M Liu
IEEE Transactions on Multimedia (TMM), 2024
Robust 3D Action Recognition through Sampling Local Appearances and Global Distributions
M Liu, H Liu, C Chen
IEEE Transactions on Multimedia (TMM), 2018
Joint Dynamic Pose Image and Space Time Reversal for Human Action Recognition from Videos
M Liu, F Meng, C Chen, S Wu
AAAI Conference on Artificial Intelligence (AAAI), 2019
3D Action Recognition Using Multi-Scale Energy-Based Global Ternary Image
M Liu, H Liu, C Chen
IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2018
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