112 citations · 250 across the 12 of their papers we have counts for
12 papers
Generative 3D Part Assembly via Part-Whole-Hierarchy Message Passing
Bi'an Du, Xiang Gao, Wei Hu +1
Generative 3D part assembly involves understanding part relationships and predicting their 6-DoF poses for assembling a realistic 3D shape. Prior work often focus on the geometry o…
InvariantOODG: Learning Invariant Features of Point Clouds for Out-of-Distribution Generalization
Zhimin Zhang, Xiang Gao, Wei Hu
The convenience of 3D sensors has led to an increase in the use of 3D point clouds in various applications. However, the differences in acquisition devices or scenarios lead to div…
Learning Latent Part-Whole Hierarchies for Point Clouds
Xiang Gao, Wei Hu, Renjie Liao
Strong evidence suggests that humans perceive the 3D world by parsing visual scenes and objects into part-whole hierarchies. Although deep neural networks have the capability of le…
Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization
Xiang Gao, Yuqi Zhang, Yingjie Tian
Image cartoonization is recently dominated by generative adversarial networks (GANs) from the perspective of unsupervised image-to-image translation, in which an inherent challenge…
StarGraph: Knowledge Representation Learning based on Incomplete Two-hop Subgraph
Hongzhu Li, Xiangrui Gao, Linhui Feng +2
Conventional representation learning algorithms for knowledge graphs (KG) map each entity to a unique embedding vector, ignoring the rich information contained in the neighborhood.…
Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learning
Bi'an Du, Xiang Gao, Wei Hu +1
Point clouds have attracted increasing attention. Significant progress has been made in methods for point cloud analysis, which often requires costly human annotation as supervisio…
Self-Supervised Graph Representation Learning via Topology Transformations
Xiang Gao, Wei Hu, Guo-Jun Qi
We present the Topology Transformation Equivariant Representation learning, a general paradigm of self-supervised learning for node representations of graph data to enable the wide…
Self-Supervised Multi-View Learning via Auto-Encoding 3D Transformations
Xiang Gao, Wei Hu, Guo-Jun Qi
3D object representation learning is a fundamental challenge in computer vision to infer about the 3D world. Recent advances in deep learning have shown their efficiency in 3D obje…
Dynamic Point Cloud Denoising via Manifold-to-Manifold Distance
Wei Hu, Qianjiang Hu, Zehua Wang +1
3D dynamic point clouds provide a natural discrete representation of real-world objects or scenes in motion, with a wide range of applications in immersive telepresence, autonomous…
GraphTER: Unsupervised Learning of Graph Transformation Equivariant Representations via Auto-Encoding Node-wise Transformations
Xiang Gao, Wei Hu, Guo-Jun Qi
Recent advances in Graph Convolutional Neural Networks (GCNNs) have shown their efficiency for non-Euclidean data on graphs, which often require a large amount of labeled data with…
Feature Graph Learning for 3D Point Cloud Denoising
Wei Hu, Xiang Gao, Gene Cheung +1
Identifying an appropriate underlying graph kernel that reflects pairwise similarities is critical in many recent graph spectral signal restoration schemes, including image denoisi…
3D Dynamic Point Cloud Denoising via Spatial-Temporal Graph Learning
Wei Hu, Qianjiang Hu, Zehua Wang +1
The prevalence of accessible depth sensing and 3D laser scanning techniques has enabled the convenient acquisition of 3D dynamic point clouds, which provide efficient representatio…