Wanqing Li

Associate Professor

University of Wollongong

Australia

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Dr. Wanqing Li is an Associate Professor at the University of Wollongong, Australia. His research focuses on skeleton-based action recognition, featuring innovative techniques such as attention-guided heterogeneous graph neural networks and deep fusion networks. Additionally, he explores advanced methodologies in semantic segmentation and depth estimation, contributing to machine learning in dynamic environments.

Wanqing Li

Research Interests

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About

Articles (20)

Spatio-Temporal Joint Density Driven Learning for Skeleton-Based Action Recognition

Skeleton-based action recognition through attention guided heterogeneous graph neural network

Unsupervised Feature Enrichment and Fidelity Preservation Learning Framework for Skeleton-Based Action Recognition

A Complex Hermitian Positive Definite Manifold Embedding Transformer Network for Time-Varying Direction of Arrival Tracking

Boosting Generalization of Semantic Segmentation With Unseen Style Seeking-Based Meta-Learning

An attention-based CNN for automatic whole-body postural assessment

Asynchronous Joint-based Temporal Pooling for Skeleton-based Action Recognition

Self-Supervised Monocular Depth Estimation via Binocular Geometric Correlation Learning

DFN: A deep fusion network for flexible single and multi-modal action recognition

Hierarchical Aggregated Graph Neural Network for Skeleton-Based Action Recognition

Modeling Long-range Dependencies and Epipolar Geometry for Multi-view Stereo

Novel View Synthesis from a Single Unposed Image via Unsupervised Learning

Region-Aware Hierarchical Latent Feature Representation Learning-Guided Clustering for Hyperspectral Band Selection

Reducing Background Induced Domain Shift for Adaptive Person Re-Identification

A Scaled Physical Model Study of Culvert Blockage Exploring Complex Relationships Between Influential Factors

Improved Shift Graph Convolutional Network for Action Recognition With Skeleton

A Two-Stream Neural Network for Pose-Based Hand Gesture Recognition

A Central Difference Graph Convolutional Operator for Skeleton-Based Action Recognition

Deep Affine Motion Compensation Network for Inter Prediction in VVC

Trear: Transformer-Based RGB-D Egocentric Action Recognition

Collaborators (5)

Pascal Perez

Philip O. Ogunbona

jie jack yang

Johan Barthélemy

Xuequan Lu

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