Jan. 31, 2024, 3:42 p.m. | Xuehui Yu Pengfei Chen Kuiran Wang Xumeng Han Guorong Li Zhenjun Han Qixiang Ye Jianbin Jiao

cs.CV updates on arXiv.org arxiv.org

Point-based object localization (POL), which pursues high-performance object sensing under low-cost data annotation, has attracted increased attention. However, the point annotation mode inevitably introduces semantic variance due to the inconsistency of annotated points. Existing POL heavily rely on strict annotation rules, which are difficult to define and apply, to handle the problem. In this study, we propose coarse point refinement (CPR), which to our best knowledge is the first attempt to alleviate semantic variance from an algorithmic perspective. CPR reduces …

annotation apply attention cost cs.cv data data annotation localization low performance rules semantic sensing supervision variance via

Founding AI Engineer, Agents

@ Occam AI | New York

AI Engineer Intern, Agents

@ Occam AI | US

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

DevOps Engineer (Data Team)

@ Reward Gateway | Sofia/Plovdiv