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Authors: Taha Alhersh and Heiner Stuckenschmidt

Affiliation: Data and Web Science Group, University of Mannheim, 68131 Mannheim and Germany

ISBN: 978-989-758-354-4

Keyword(s): Optical Flow, Unsupervised Learning, Fine-tuning, Motion Boundary, Deep Learning.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Motion, Tracking and Stereo Vision ; Optical Flow and Motion Analyses

Abstract: Recently, convolutional neural network (CNN) based approaches have proven to be successful in optical flow estimation in the supervised as well as in the unsupervised training paradigms. Supervised training requires large amounts of training data with task specific motion statistics. Usually, synthetic datasets are used for this purpose. Fully unsupervised approaches are usually harder to train and show weaker performance, although they have access to the true data statistics during training. In this paper we exploit a well-performing pre-trained model and fine-tune it in an unsupervised way using classical optical flow training objectives to learn the dataset specific statistics. Thus, per dataset training time can be reduced from days to less than 1 minute. Specifically, motion boundaries estimated by gradients in the optical flow field can be greatly improved using the proposed unsupervised fine-tuning.

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Paper citation in several formats:
Alhersh, T. and Stuckenschmidt, H. (2019). Unsupervised Fine-tuning of Optical Flow for Better Motion Boundary Estimation.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 776-783. DOI: 10.5220/0007343707760783

@conference{visapp19,
author={Taha Alhersh. and Heiner Stuckenschmidt.},
title={Unsupervised Fine-tuning of Optical Flow for Better Motion Boundary Estimation},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
year={2019},
pages={776-783},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007343707760783},
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 - Unsupervised Fine-tuning of Optical Flow for Better Motion Boundary Estimation
SN - 978-989-758-354-4
AU - Alhersh, T.
AU - Stuckenschmidt, H.
PY - 2019
SP - 776
EP - 783
DO - 10.5220/0007343707760783

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