most citedLabel-efficient Segmentation via Affinity Propagation

3 citations · 9 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CV2024

ReliOcc: Towards Reliable Semantic Occupancy Prediction via Uncertainty Learning

Song Wang, Zhongdao Wang, Jiawei Yu +4

Vision-centric semantic occupancy prediction plays a crucial role in autonomous driving, which requires accurate and reliable predictions from low-cost sensors. Although having not…

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.CL20241 cited

Uncovering Misattributed Suicide Causes through Annotation Inconsistency Detection in Death Investigation Notes

Song Wang, Yiliang Zhou, Ziqiang Han +5

Data accuracy is essential for scientific research and policy development. The National Violent Death Reporting System (NVDRS) data is widely used for discovering the patterns and…

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.SE2023

A First Look at Fairness of Machine Learning Based Code Reviewer Recommendation

Mohammad Mahdi Mohajer, Alvine Boaye Belle, Nima Shiri harzevili +5

The fairness of machine learning (ML) approaches is critical to the reliability of modern artificial intelligence systems. Despite extensive study on this topic, the fairness of ML…