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Incorporation of Extra Pseudo Labels for CNN-based Gait Recognition

dc.contributor.authorDaigo Muramatsu, Kousuke Moriwaki
dc.contributor.editorBrömme, Arslan
dc.contributor.editorDamer, Naser
dc.contributor.editorGomez-Barrero, Marta
dc.contributor.editorRaja, Kiran
dc.contributor.editorRathgeb, Christian
dc.contributor.editorSequeira Ana F.
dc.contributor.editorTodisco, Massimiliano
dc.contributor.editorUhl, Andreas
dc.date.accessioned2022-10-27T10:19:28Z
dc.date.available2022-10-27T10:19:28Z
dc.date.issued2022
dc.description.abstractCNN is a major model used for image-based recognition tasks, including gait recognition, and many CNN-based network structures and/or learning frameworks have been proposed. Among them, we focus on approaches that use multiple labels for learning, typified by multi-task learning. These approaches are sometimes used to improve the accuracy of the main task by incorporating extra labels associated with sub-tasks. The incorporated labels for learning are usually selected from real tasks heuristically; for example, gender and/or age labels are incorporated together with subject identity labels.We take a different approach and consider a virtual task as a sub-task, and incorporate pseudo output labels together with labels associated with the main task and/or real task. In this paper, we focus on a gait-based person recognition task as the main task, and we discuss the effectiveness of virtual tasks with different pseudo labels for construction of a CNN-based gait feature extractor.en
dc.identifier.doi10.1109/BIOSIG55365.2022.9897053
dc.identifier.isbn978-3-88579-723-4
dc.identifier.pissn1617-5488
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/39698
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofBIOSIG 2022
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-329
dc.subjectGait Recognition
dc.subjectAttribute
dc.subjectPseudo label
dc.subjectCNN
dc.titleIncorporation of Extra Pseudo Labels for CNN-based Gait Recognitionen
dc.typeText/Conference Paper
gi.citation.endPage220
gi.citation.publisherPlaceBonn
gi.citation.startPage213
gi.conference.date14.-16. September 2022
gi.conference.locationDarmstadt
gi.conference.sessiontitleFurther Conference Contributions

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