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Authors: Angel Villar-Corrales and Sven Behnke

Affiliation: Autonomous Intelligent Systems, University of Bonn, Germany

Keyword(s): Object-Centric Representation Learning, Unsupervised Image Decomposition, Frequency-Domain Neural Networks, Phase Correlation.

Abstract: The ability to decompose scenes into their object components is a desired property for autonomous agents, allowing them to reason and act in their surroundings. Recently, different methods have been proposed to learn object-centric representations from data in an unsupervised manner. These methods often rely on latent representations learned by deep neural networks, hence requiring high computational costs and large amounts of curated data. Such models are also difficult to interpret. To address these challenges, we propose the Phase-Correlation Decomposition Network (PCDNet), a novel model that decomposes a scene into its object components, which are represented as transformed versions of a set of learned object prototypes. The core building block in PCDNet is the Phase-Correlation Cell (PC Cell), which exploits the frequency-domain representation of the images in order to estimate the transformation between an object prototype and its transformed version in the image. In our experi ments, we show how PCDNet outperforms state-of-the-art methods for unsupervised object discovery and segmentation on simple benchmark datasets and on more challenging data, while using a small number of learnable parameters and being fully interpretable. Code and models to reproduce our experiments can be found in https://github.com/AIS-Bonn/Unsupervised-Decomposition-PCDNet. (More)

CC BY-NC-ND 4.0

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Paper citation in several formats:
Villar-Corrales, A. and Behnke, S. (2022). Unsupervised Image Decomposition with Phase-Correlation Networks. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 224-235. DOI: 10.5220/0010919100003124

@conference{visapp22,
author={Angel Villar{-}Corrales. and Sven Behnke.},
title={Unsupervised Image Decomposition with Phase-Correlation Networks},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP},
year={2022},
pages={224-235},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010919100003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP
TI - Unsupervised Image Decomposition with Phase-Correlation Networks
SN - 978-989-758-555-5
IS - 2184-4321
AU - Villar-Corrales, A.
AU - Behnke, S.
PY - 2022
SP - 224
EP - 235
DO - 10.5220/0010919100003124
PB - SciTePress