Teachers
Current: Home > Innovation team > Teachers >

Mengyuan Liu PhD supervisor

刘梦源

Research direction:Deep Learning, Computer Vision, Video Analysis and Understanding

Education background

2012-2017  Peking University  Doctor
2017-2018  Nanyang Technological University  Postdoc
2018-2020  Tencent Technology   Senior researcher
2020-2023  Sun Yat-Sen University  Associate Professor
2023-Now  Peking University  PhD supervisor

Emailnkliuyifang@gmail.com

Published papers

# Auhtor Conference|Journal Paper title
1 Wei Shi, Hong Liu, Mengyuan Liu Pattern Recognition Image-to-video person re-identification using three-dimensional semantic appearance alignment and cross-modal interactive learning
2 Mengyuan Liu, Youneng Bao, Yongsheng Liang, Fanyang Meng IEEE Signal Processing Letters Spatial-Temporal Asynchronous Normalization for Unsupervised 3D Action Representation Learning
3 Wenhao Li, Hong Liu, Runwei Ding, Mengyuan Liu, Pichao Wang, Wenming Yang IEEE Transactions on Multimedia Exploiting temporal contexts with strided transformer for 3d human pose estimation
4 Xuan Wang, Minghong Zhong, Hoiyuen Cheng, Junjie Xie, Yingchu Zhou, Jun Ren, Mengyuan Liu CAAI Transactions on Intelligence Technology SpikeGoogle: Spiking Neural Networks with GoogLeNet-like inception module
5 Wei Shi, Hong Liu, Mengyuan Liu Image and Vision Computing IRANet: Identity-relevance aware representation for cloth-changing person re-identification
6 Yi Zhang, Youjun Zhao, Yuhang Wen, Zixuan Tang, Xinhua Xu, Mengyuan Liu Proceedings of the 29th ACM International Conference on Multimedia Facial Prior Based First Order Motion Model for Micro-expression Generation
7 Yang Liu, Huaqiu Wang, Fanyang Meng, Mengyuan Liu, Hong Liu 2021 IEEE International Conference on Image Processing (ICIP) Attend, Correct And Focus: A Bidirectional Correct Attention Network For Image-Text Matching
8 Wei Shi, Hong Liu, Mengyuan Liu Neurocomputing Identity-sensitive loss guided and instance feature boosted deep embedding for person search
9 Hong Liu, Bin Ren, Mengyuan Liu, Runwei Ding IEEE International Conference on Image Processing (ICIP) Grouped Temporal Enhancement Module for Human Action Recognition
10 Hong Liu, Linlin Zhang, Lisi Guan, Mengyuan Liu IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) GFNET: A Lightweight Group Frame Network for Efficient Human Action Recognition
11 Liu M, Liu H, Chen C Pattern Recognition Enhanced skeleton visualization for view invariant human action recognition[
12 Liu M, Liu H, Chen C IEEE Transactions on Circuits & Systems for Video Technology 3D Action Recognition Using Multi-scale Energy-based Global Ternary Image
13 Liu M, Liu H Neurocomputing Depth Context: a new descriptor for human activity recognition by using sole depth sequences
14 Sun Q, Liu H, Liu M Neurocomputing Human activity prediction by mapping grouplets to recurrent Self-Organizing Map
15 Liu M, Chen C, Liu H IEEE International Conference on Multimedia and Expo LEARNING INFORMATIVE PAIRWISE JOINTS WITH ENERGY-BASED TEMPORAL PYRAMID FOR 3D ACTION RECOGNITION
16 Liu M, Chen C, Liu H IEEE International Conference on Multimedia and Expo 3D ACTION RECOGNITION USING DATA VISUALIZATION AND CONVOLUTIONAL NEURAL NETWORKS
17 Liu M, Liu H, Sun Q IEEE International Conference on Multimedia and Expo Action classification by exploring directional co-occurrence of weighted stips
18 Liu M, Liu H, Chen C International Conference on 3d Vision. IEEE Energy-Based Global Ternary Image for Action Recognition Using Sole Depth Sequences
19 Liu M, Chen C, Meng F 3D ACTION RECOGNITION USING MULTI-TEMPORAL SKELETON VISUALIZATION
20 Liu M, Liu H, Sun Q Caai Transactions on Intelligence Technology Salient pairwise spatio-temporal interest points for real-time activity recognition
21 Chen C, Liu M, Zhang B International Joint Conference on Artificial Intelligence. AAAI Press 3D action recognition using multi-temporal depth motion maps and fisher vector
22 Liu H, Liu M, Sun Q Learning directional co-occurrence for human action classification
23 Liu M, Liu H, Chen C IEEE Transactions on Multimedia (T-MM) Robust 3D Action Recognition through Sampling Local Appearances and Global Distributions