Deep Active Learning with Noisy Oracle in Object Detection

Marius Schubert, Tobias Riedlinger, Karsten Kahl, Matthias Rottmann

2024

Abstract

Obtaining annotations for complex computer vision tasks such as object detection is an expensive and timeintense endeavor involving numerous human workers or expert opinions. Reducing the amount of annotations required while maintaining algorithm performance is, therefore, desirable for machine learning practitioners and has been successfully achieved by active learning. However, it is not merely the amount of annotations which influences model performance but also the annotation quality. In practice, oracles that are queried for new annotations frequently produce significant amounts of noise. Therefore, cleansing procedures are oftentimes necessary to review and correct given labels. This process is subject to the same budget as the initial annotation itself since it requires human workers or even domain experts. Here, we propose a composite active learning framework including a label review module for deep object detection. We show that utilizing part of the annotation budget to correct the noisy annotations partially in the active dataset leads to early improvements in model performance, especially when coupled with uncertainty-based query strategies. The precision of the label error proposals significantly influences the measured effect of the label review. In our experiments we achieve improvements of up to 4.5mAP points by incorporating label reviews at equal annotation budget.

Paper Citation

in Harvard Style

Schubert M., Riedlinger T., Kahl K. and Rottmann M. (2024). Deep Active Learning with Noisy Oracle in Object Detection. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 375-384. DOI: 10.5220/0012315800003660

in Bibtex Style

@conference{visapp24,
author={Marius Schubert and Tobias Riedlinger and Karsten Kahl and Matthias Rottmann},
title={Deep Active Learning with Noisy Oracle in Object Detection},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2024},
pages={375-384},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012315800003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Deep Active Learning with Noisy Oracle in Object Detection
SN - 978-989-758-679-8
AU - Schubert M.
AU - Riedlinger T.
AU - Kahl K.
AU - Rottmann M.
PY - 2024
SP - 375
EP - 384
DO - 10.5220/0012315800003660
PB - SciTePress