Web: http://arxiv.org/abs/2209.11159

Sept. 23, 2022, 1:15 a.m. | Klara Janouskova, Mattia Rigotti, Ioana Giurgiu, Cristiano Malossi

cs.CV updates on arXiv.org arxiv.org

Labeling images for visual segmentation is a time-consuming task which can be
costly, particularly in application domains where labels have to be provided by
specialized expert annotators, such as civil engineering. In this paper, we
propose to use attribution methods to harness the valuable interactions between
expert annotators and the data to be annotated in the case of defect
segmentation for visual inspection of civil infrastructures. Concretely, a
classifier is trained to detect defects and coupled with an attribution-based
method …

arxiv explainability labeling

More from arxiv.org / cs.CV updates on arXiv.org

Postdoctoral Fellow: ML for autonomous materials discovery

@ Lawrence Berkeley National Lab | Berkeley, CA

Research Scientists

@ ODU Research Foundation | Norfolk, Virginia

Embedded Systems Engineer (Robotics)

@ Neo Cybernetica | Bedford, New Hampshire

2023 Luis J. Alvarez and Admiral Grace M. Hopper Postdoc Fellowship in Computing Sciences

@ Lawrence Berkeley National Lab | San Francisco, CA

Senior Manager Data Scientist

@ NAV | Remote, US

Senior AI Research Scientist

@ Earth Species Project | Remote anywhere

Research Fellow- Center for Security and Emerging Technology (Multiple Opportunities)

@ University of California Davis | Washington, DC

Staff Fellow - Data Scientist

@ U.S. FDA/Center for Devices and Radiological Health | Silver Spring, Maryland

Staff Fellow - Senior Data Engineer

@ U.S. FDA/Center for Devices and Radiological Health | Silver Spring, Maryland

Research Engineer - VFX, Neural Compositing

@ Flawless | Los Angeles, California, United States

[Job-TB] Senior Data Engineer

@ CI&T | Brazil

Data Analytics Engineer

@ The Fork | Paris, France