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dc.contributor.author
Caelles Prat, Sergi
dc.contributor.supervisor
Van Gool, Luc
dc.contributor.supervisor
Leibe, Bastian
dc.contributor.supervisor
Pont-Tuset, Jordi
dc.date.accessioned
2021-02-08T08:06:57Z
dc.date.available
2021-02-07T19:21:14Z
dc.date.available
2021-02-08T08:06:57Z
dc.date.issued
2020
dc.identifier.uri
http://hdl.handle.net/20.500.11850/468189
dc.identifier.doi
10.3929/ethz-b-000468189
dc.description.abstract
Video Object Segmentation (VOS) has been one of the several tasks where deep learning has brought enormous performance gains. This task consists in segmenting the objects in a video, that is, grouping together pixels that belong to the same object, both in space (within a frame) and in time (across different frames). Sergi's dissertation focuses on two important aspects to advance the progress in the topic: it proposes new VOS methods that advance the state of the art and it releases new datasets and benchmarks on which such techniques can be trained and compared.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
ETH Zurich
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
Segmentation
en_US
dc.subject
Deep learning
en_US
dc.subject
Video analysis
en_US
dc.title
Video Object Segmentation: Methods and Datasets
en_US
dc.type
Doctoral Thesis
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2021-02-08
ethz.size
124 p.
en_US
ethz.code.ddc
DDC - DDC::0 - Computer science, information & general works::004 - Data processing, computer science
en_US
ethz.identifier.diss
27155
en_US
ethz.publication.place
Zurich
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02652 - Institut für Bildverarbeitung / Computer Vision Laboratory::03514 - Van Gool, Luc / Van Gool, Luc
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02652 - Institut für Bildverarbeitung / Computer Vision Laboratory::03514 - Van Gool, Luc / Van Gool, Luc
en_US
ethz.date.deposited
2021-02-07T19:21:33Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-02-08T08:07:09Z
ethz.rosetta.lastUpdated
2022-03-29T05:06:42Z
ethz.rosetta.versionExported
true
ethz.COinS
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