Unsupervised domain adaptation (UDA) aims to train a model on a labeled source domain
and adapt it to an unlabeled target domain. In medical image segmentation field, most …

Unsupervised domain adaptation (UDA) could significantly improve the cross-domain
performance of current supervised 3D deep learning methods and have a widespread …

This paper presents a comprehensive study of 3D point cloud Federated Few-Shot Learning
(3DFFL), focusing on addressing challenges such as limited data availability and privacy …

This paper focuses on the sim-to-real issue of RGB-D grasp detection and formulates it as a
domain adaptation problem. In this case, we present a global-to-local method to address …

In this letter, we propose a novel Grasp Pose Domain Adaptation Network (GPDAN) to
achieve sim-to-real domain adaptation for 6-DoF grasp pose detection. The main task of …

As a significant step for human face modeling editing and generation face landmarking aims
at extracting facial keypoints from images. A generalizable face landmarker is required in …

Point cloud registration is a hot research topic in the field of computer vision. In recent years,
the registration method based on evolutionary computation has attracted more and more …

The expensive fine-grained annotation and data scarcity have become the primary
obstacles for the widespread adoption of deep learning-based Whole Slide Images (WSI) …

As important geospatial data, point cloud collected from an aerial laser scanner (ALS)
provides three-dimensional (3D) information for the study of the distribution of typical urban …

The segmentation of airborne lidar scanning (ALS) point clouds is one of the basic tasks in
remote sensing field. The existed learning-based methods have acquired satisfactory …