loading
Documents

Research.Publish.Connect.

Paper

Authors: Clemens-Alexander Brust 1 ; Christoph Käding 2 and Joachim Denzler 2

Affiliations: 1 Computer Vision Group, Friedrich Schiller University Jena, Germany ; 2 Computer Vision Group, Friedrich Schiller University Jena, Germany, Michael Stifel Center Jena, Germany

ISBN: 978-989-758-354-4

Keyword(s): Active Learning, Deep Learning, Object Detection, YOLO, Continuous Learning, Incremental Learning.

Abstract: The great success that deep models have achieved in the past is mainly owed to large amounts of labeled training data. However, the acquisition of labeled data for new tasks aside from existing benchmarks is both challenging and costly. Active learning can make the process of labeling new data more efficient by selecting unlabeled samples which, when labeled, are expected to improve the model the most. In this paper, we combine a novel method of active learning for object detection with an incremental learning scheme (Käding et al., 2016b) to enable continuous exploration of new unlabeled datasets. We propose a set of uncertainty-based active learning metrics suitable for most object detectors. Furthermore, we present an approach to leverage class imbalances during sample selection. All methods are evaluated systematically in a continuous exploration context on the PASCAL VOC 2012 dataset (Everingham et al., 2010).

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.205.109.82

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Brust, C.; Käding, C. and Denzler, J. (2019). Active Learning for Deep Object Detection.In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, ISBN 978-989-758-354-4, pages 181-190. DOI: 10.5220/0007248601810190

@conference{visapp19,
author={Clemens{-}Alexander Brust. and Christoph Käding. and Joachim Denzler.},
title={Active Learning for Deep Object Detection},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
year={2019},
pages={181-190},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007248601810190},
isbn={978-989-758-354-4},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,
TI - Active Learning for Deep Object Detection
SN - 978-989-758-354-4
AU - Brust, C.
AU - Käding, C.
AU - Denzler, J.
PY - 2019
SP - 181
EP - 190
DO - 10.5220/0007248601810190

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.