Point cloud learning has lately attracted increasing attention due to its wide applications in
many areas, such as computer vision, autonomous driving, and robotics. As a dominating …

Multi-view learning (MVL) has attracted increasing attention and achieved great practical
success by exploiting complementary information of multiple features or modalities …

Recently, zero-shot and few-shot learning via Contrastive Vision-Language Pre-training
(CLIP) have shown inspirational performance on 2D visual recognition, which learns to …

Most prior work represents the shapes of point clouds by coordinates. However, it is
insufficient to describe the local geometry directly. In this paper, we present RepSurf …

Graph Neural Networks have attracted increasing attention in recent years. However,
existing GNN frameworks are deployed based upon simple graphs, which limits their …

Recently, directly detecting 3D objects from 3D point clouds has received increasing
attention. To extract object representation from an irregular point cloud, existing methods …

Processing point cloud data is an important component of many real-world systems. As
such, a wide variety of point-based approaches have been proposed, reporting steady …

Nowadays, pre-training big models on large-scale datasets has become a crucial topic in
deep learning. The pre-trained models with high representation ability and transferability …

Point cloud analysis is very challenging, as the shape implied in irregular points is difficult to
capture. In this paper, we propose RS-CNN, namely, Relation-Shape Convolutional Neural …

In this paper, we present a hypergraph neural networks (HGNN) framework for data
representation learning, which can encode high-order data correlation in a hypergraph …