13 citations · 19 across the 11 of their papers we have counts for
11 papers
Gaussian-Mixture Latent Flow for Stochastic 3D Human Motion Prediction
Yue Ma, Frederick W. B. Li, Xiaohui Liang
Stochastic human motion prediction aims to forecast future motion distributions. Although recent studies have achieved strong performance in terms of accuracy and diversity, they o…
NeuroPath: Brain-Inspired Dual-Pathway Graph Convolutional Networks for Skeleton-Based Action Recognition
Kanglei Zhou, Ruizhi Cai, Hubert P. H. Shum +2
Skeleton-based action recognition aims to recognize human actions from sequences of human joint coordinates. Most existing Spatial-Temporal Graph Convolutional Networks (STGCNs) ha…
ST-SACLF: Style Transfer Informed Self-Attention Classifier for Bias-Aware Painting Classification
Mridula Vijendran, Frederick W. B. Li, Jingjing Deng +1
Painting classification plays a vital role in organizing, finding, and suggesting artwork for digital and classic art galleries. Existing methods struggle with adapting knowledge f…
From Category to Scenery: An End-to-End Framework for Multi-Person Human-Object Interaction Recognition in Videos
Tanqiu Qiao, Ruochen Li, Frederick W. B. Li +1
Video-based Human-Object Interaction (HOI) recognition explores the intricate dynamics between humans and objects, which are essential for a comprehensive understanding of human be…
MAGR: Manifold-Aligned Graph Regularization for Continual Action Quality Assessment
Kanglei Zhou, Liyuan Wang, Xingxing Zhang +4
Action Quality Assessment (AQA) evaluates diverse skills but models struggle with non-stationary data. We propose Continual AQA (CAQA) to refine models using sparse new data. Featu…
Fg-T2M: Fine-Grained Text-Driven Human Motion Generation via Diffusion Model
Yin Wang, Zhiying Leng, Frederick W. B. Li +2
Text-driven human motion generation in computer vision is both significant and challenging. However, current methods are limited to producing either deterministic or imprecise moti…
Tackling Data Bias in Painting Classification with Style Transfer
Mridula Vijendran, Frederick W. B. Li, Hubert P. H. Shum
It is difficult to train classifiers on paintings collections due to model bias from domain gaps and data bias from the uneven distribution of artistic styles. Previous techniques…
Geometric Features Informed Multi-person Human-object Interaction Recognition in Videos
Tanqiu Qiao, Qianhui Men, Frederick W. B. Li +3
Human-Object Interaction (HOI) recognition in videos is important for analyzing human activity. Most existing work focusing on visual features usually suffer from occlusion in the…
Distillation of Human-Object Interaction Contexts for Action Recognition
Muna Almushyti, Frederick W. Li
Modeling spatial-temporal relations is imperative for recognizing human actions, especially when a human is interacting with objects, while multiple objects appear around the human…
Multi-Scale Dual-Branch Fully Convolutional Network for Hand Parsing
Yang Lu, Xiaohui Liang, Frederick W. B. Li
Recently, fully convolutional neural networks (FCNs) have shown significant performance in image parsing, including scene parsing and object parsing. Different from generic object…
DOOBNet: Deep Object Occlusion Boundary Detection from an Image
Guoxia Wang, Xiaohui Liang, Frederick W. B. Li
Object occlusion boundary detection is a fundamental and crucial research problem in computer vision. This is challenging to solve as encountering the extreme boundary/non-boundary…