Deep supervised learning algorithms typically require a large volume of labeled data to
achieve satisfactory performance. However, the process of collecting and labeling such data …

Point cloud data have been widely explored due to its superior accuracy and robustness
under various adverse situations. Meanwhile, deep neural networks (DNNs) have achieved …

Large-scale labeled data are generally required to train deep neural networks in order to
obtain better performance in visual feature learning from images or videos for computer …

Segmenting or detecting objects in sparse Lidar point clouds are two important tasks in
autonomous driving to allow a vehicle to act safely in its 3D environment. The best …

Recently, Self-Supervised Representation Learning (SSRL) has attracted much attention in
the field of computer vision, speech, natural language processing (NLP), and recently, with …

Multimodal learning, which aims to understand and analyze information from multiple
modalities, has achieved substantial progress in the supervised regime in recent years …

Abstract 3D point clouds are a crucial type of data collected by LiDAR sensors and widely
used in transportation applications due to its concise descriptions and accurate localization …

Point cloud data has been extensively studied due to its compact form and flexibility in
representing complex 3D structures. The ability of point cloud data to accurately capture and …

Recently, the pre-training paradigm combining Transformer and masked language modeling
in BERT has achieved tremendous success not only in NLP, but also in images and point …

Deep supervised learning algorithms typically require a large volume of labeled data to
achieve satisfactory performance. However, the process of collecting and labeling such data …