Web: http://arxiv.org/abs/2204.00147

June 20, 2022, 1:13 a.m. | Akhil Meethal, Marco Pedersoli, Zhongwen Zhu, Francisco Perdigon Romero, Eric Granger

cs.CV updates on arXiv.org arxiv.org

Semi- and weakly-supervised learning have recently attracted considerable
attention in the object detection literature since they can alleviate the cost
of annotation needed to successfully train deep learning models. State-of-art
approaches for semi-supervised learning rely on student-teacher models trained
using a multi-stage process, and considerable data augmentation. Custom
networks have been developed for the weakly-supervised setting, making it
difficult to adapt to different detectors. In this paper, a weakly
semi-supervised training method is introduced that reduces these training
challenges, yet …

arxiv cv detection sampling

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

Machine Learning Researcher - Saalfeld Lab

@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia

Project Director, Machine Learning in US Health

@ ideas42.org | Remote, US

Data Science Intern

@ NannyML | Remote

Machine Learning Engineer NLP/Speech

@ Play.ht | Remote

Research Scientist, 3D Reconstruction

@ Yembo | Remote, US

Clinical Assistant or Associate Professor of Management Science and Systems

@ University at Buffalo | Buffalo, NY