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20212024
most citedBox-supervised Instance Segmentation with Level Set Evolution

7 citations · 18 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV20242 cited

Not All Voxels Are Equal: Hardness-Aware Semantic Scene Completion with Self-Distillation

Song Wang, Jiawei Yu, Wentong Li +4

Semantic scene completion, also known as semantic occupancy prediction, can provide dense geometric and semantic information for autonomous vehicles, which attracts the increasing…

cs.CV20241 cited

MGMap: Mask-Guided Learning for Online Vectorized HD Map Construction

Xiaolu Liu, Song Wang, Wentong Li +3

Currently, high-definition (HD) map construction leans towards a lightweight online generation tendency, which aims to preserve timely and reliable road scene information. However,…

cs.CV20233 cited

Label-efficient Segmentation via Affinity Propagation

Wentong Li, Yuqian Yuan, Song Wang +5

Weakly-supervised segmentation with label-efficient sparse annotations has attracted increasing research attention to reduce the cost of laborious pixel-wise labeling process, whil…

cs.CV20232 cited

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…

cs.CV20231 cited

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…

cs.CV20227 cited

Box-supervised Instance Segmentation with Level Set Evolution

Wentong Li, Wenyu Liu, Jianke Zhu +3

In contrast to the fully supervised methods using pixel-wise mask labels, box-supervised instance segmentation takes advantage of the simple box annotations, which has recently att…