1 citations · 1 across the 5 of their papers we have counts for
5 papers
PanDA: Unsupervised Domain Adaptation for Multimodal 3D Panoptic Segmentation in Autonomous Driving
Yining Pan, Shijie Li, Yuchen Wu +2
This paper presents the first study on Unsupervised Domain Adaptation (UDA) for multimodal 3D panoptic segmentation (mm-3DPS), aiming to improve generalization under domain shifts…
Few-Shot Incremental 3D Object Detection in Dynamic Indoor Environments
Yun Zhu, Jianjun Qian, Jian Yang +2
Incremental 3D object perception is a critical step toward embodied intelligence in dynamic indoor environments. However, existing incremental 3D detection methods rely on extensiv…
SCOPE: Scene-Contextualized Incremental Few-Shot 3D Segmentation
Vishal Thengane, Zhaochong An, Tianjin Huang +5
Incremental Few-Shot (IFS) segmentation aims to learn new categories over time from only a few annotations. Although widely studied in 2D, it remains underexplored for 3D point clo…
Dual-supervised Asymmetric Co-training for Semi-supervised Medical Domain Generalization
Jincai Song, Haipeng Chen, Jun Qin +1
Semi-supervised domain generalization (SSDG) in medical image segmentation offers a promising solution for generalizing to unseen domains during testing, addressing domain shift ch…
Uncertainty Meets Diversity: A Comprehensive Active Learning Framework for Indoor 3D Object Detection
Jiangyi Wang, Na Zhao
Active learning has emerged as a promising approach to reduce the substantial annotation burden in 3D object detection tasks, spurring several initiatives in outdoor environments.…