With the success of the 3D deep learning models, various perception technologies for
autonomous driving have been developed in the LiDAR domain. While these models …

Referring expression segmentation aims to segment an object described by a language
expression from an image. Despite the recent progress on this task, existing models tackling …

In this paper, we propose an unsupervised domain adaptation method for deep point cloud
representation learning. To model the internal structures in target point clouds, we first …

Video scene graph generation (VidSGG) aims to parse the video content into scene graphs,
which involves modeling the spatio-temporal contextual information in the video. However …

Domain generalization (DG) aims at solving distribution shift problems in various scenes.
Existing approaches are based on Convolution Neural Networks (CNNs) or Vision …

In the past decade, deep neural networks have achieved significant progress in point cloud
learning. However, collecting large-scale precisely-annotated point clouds is extremely …

Abstract Machine unlearning (MU) seeks to remove knowledge of specific data samples
from trained models without the necessity for complete retraining, a task made challenging …

Using deep learning, 3D autonomous driving semantic segmentation has become a well-
studied subject, with methods that can reach very high performance. Nonetheless, because …

We consider a new problem of adapting a human mesh reconstruction model to out-of-
domain streaming videos, where the performance of existing SMPL-based models is …

Deep networks on 3D point clouds have achieved remarkable success in 3D classification,
while they are vulnerable to geometry variations caused by inconsistent data acquisition …