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Object Segmentation Using Pixel-Wise Adversarial Loss

  • Recent deep learning based approaches have shown remarkable success on object segmentation tasks. However, there is still room for further improvement. Inspired by generative adversarial networks, we present a generic end-to-end adversarial approach, which can be combined with a wide range of existing semantic segmentation networks to improve their segmentation performance. The key element of ourRecent deep learning based approaches have shown remarkable success on object segmentation tasks. However, there is still room for further improvement. Inspired by generative adversarial networks, we present a generic end-to-end adversarial approach, which can be combined with a wide range of existing semantic segmentation networks to improve their segmentation performance. The key element of our method is to replace the commonly used binary adversarial loss with a high resolution pixel-wise loss. In addition, we train our generator employing stochastic weight averaging fashion, which further enhances the predicted output label maps leading to state-of-the-art results. We show, that this combination of pixel-wise adversarial training and weight averaging leads to significant and consistent gains in segmentation performance, compared to the baseline models.show moreshow less

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Metadaten
Document Type:Conference Proceeding
Conference Type:Konferenzartikel
Zitierlink: https://opus.hs-offenburg.de/3954
Bibliografische Angaben
Title (English):Object Segmentation Using Pixel-Wise Adversarial Loss
Conference:41st DAGM German Conference (DAGM GCPR 2019), September 10-13, 2019, Dortmund, Germany
Author:Ricard Durall Lopez, Franz-Josef Pfreundt, Janis KeuperStaff MemberORCiDGND
Year of Publication:2019
Place of publication:Cham
Publisher:Springer
First Page:303
Last Page:316
Parent Title (English):DAGM GCPR 2019: Pattern Recognition
Volume:LNCS 11824
ISBN:978-3-030-33676-9 (Online)
ISBN:978-3-030-33675-2 (Print)
ISSN:1611-3349 (Online)
ISSN:0302-9743 (Print)
DOI:https://doi.org/10.1007/978-3-030-33676-9_21
Language:English
Inhaltliche Informationen
Institutes:Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019)
Institutes:Bibliografie
DDC classes:000 Allgemeines, Informatik, Informationswissenschaft
Formale Angaben
Open Access: Closed Access 
Licence (German):License LogoUrheberrechtlich geschützt