52 citations · 78 across the 9 of their papers we have counts for
9 papers · 1 filter
Point2Mask: Point-supervised Panoptic Segmentation via Optimal Transport
Wentong Li, Yuqian Yuan, Song Wang +4
Weakly-supervised image segmentation has recently attracted increasing research attentions, aiming to avoid the expensive pixel-wise labeling. In this paper, we present an effectiv…
LiDAR2Map: In Defense of LiDAR-Based Semantic Map Construction Using Online Camera Distillation
Song Wang, Wentong Li, Wenyu Liu +2
Semantic map construction under bird's-eye view (BEV) plays an essential role in autonomous driving. In contrast to camera image, LiDAR provides the accurate 3D observations to pro…
Few-Shot 3D Point Cloud Semantic Segmentation via Stratified Class-Specific Attention Based Transformer Network
Canyu Zhang, Zhenyao Wu, Xinyi Wu +2
3D point cloud semantic segmentation aims to group all points into different semantic categories, which benefits important applications such as point cloud scene reconstruction and…
Parametric Surface Constrained Upsampler Network for Point Cloud
Pingping Cai, Zhenyao Wu, Xinyi Wu +1
Designing a point cloud upsampler, which aims to generate a clean and dense point cloud given a sparse point representation, is a fundamental and challenging problem in computer vi…
Cross-domain Few-shot Segmentation with Transductive Fine-tuning
Yuhang Lu, Xinyi Wu, Zhenyao Wu +1
Few-shot segmentation (FSS) expects models trained on base classes to work on novel classes with the help of a few support images. However, when there exists a domain gap between t…
G2NetPL: Generic Game-Theoretic Network for Partial-Label Image Classification
Rabab Abdelfattah, Xin Zhang, Mostafa M. Fouda +2
Multi-label image classification aims to predict all possible labels in an image. It is usually formulated as a partial-label learning problem, since it could be expensive in pract…
PLMCL: Partial-Label Momentum Curriculum Learning for Multi-Label Image Classification
Rabab Abdelfattah, Xin Zhang, Zhenyao Wu +3
Multi-label image classification aims to predict all possible labels in an image. It is usually formulated as a partial-label learning problem, given the fact that it could be expe…
CRFormer: A Cross-Region Transformer for Shadow Removal
Jin Wan, Hui Yin, Zhenyao Wu +3
Aiming to restore the original intensity of shadow regions in an image and make them compatible with the remaining non-shadow regions without a trace, shadow removal is a very chal…
Meta-RangeSeg: LiDAR Sequence Semantic Segmentation Using Multiple Feature Aggregation
Song Wang, Jianke Zhu, Ruixiang Zhang
LiDAR sensor is essential to the perception system in autonomous vehicles and intelligent robots. To fulfill the real-time requirements in real-world applications, it is necessary…