Semi-Automatic Pipeline for Large-Scale Dataset Annotation Task: A DMD Application
Abstract
This paper concerns a methodology of a semi-automatic annotation strategy for the gaze estimation material of the Driver Monitoring Dataset (DMD). It consists of a pipeline of semi-automatic annotation that uses ideas from Active Learning to annotate data with an accuracy as high as possible using less human intervention. A dummy model (the initial model) that is improved by iterative training and other state-of-the-art (SoA) models are the actors of an automatic label assessment strategy that will annotate new material. The newly annotated data will be used as an iterative process to train the dummy model and repeat the loop. The results show a reduction of annotation work for the human by 60%, where the automatically annotated images have a reliability of 99%.
Cite
Text
Urselmann et al. "Semi-Automatic Pipeline for Large-Scale Dataset Annotation Task: A DMD Application." European Conference on Computer Vision Workshops, 2022. doi:10.1007/978-3-031-25075-0_38Markdown
[Urselmann et al. "Semi-Automatic Pipeline for Large-Scale Dataset Annotation Task: A DMD Application." European Conference on Computer Vision Workshops, 2022.](https://mlanthology.org/eccvw/2022/urselmann2022eccvw-semiautomatic/) doi:10.1007/978-3-031-25075-0_38BibTeX
@inproceedings{urselmann2022eccvw-semiautomatic,
title = {{Semi-Automatic Pipeline for Large-Scale Dataset Annotation Task: A DMD Application}},
author = {Urselmann, Teun and Cañas, Paola Natalia and Ortega, Juan Diego and Nieto, Marcos},
booktitle = {European Conference on Computer Vision Workshops},
year = {2022},
pages = {560-574},
doi = {10.1007/978-3-031-25075-0_38},
url = {https://mlanthology.org/eccvw/2022/urselmann2022eccvw-semiautomatic/}
}