Show simple item record

dc.contributor.author
Sakaridis, Christos
dc.contributor.author
Dai, Dengxin
dc.contributor.author
Hecker, Simon
dc.contributor.author
Van Gool, Luc
dc.contributor.editor
Ferrari, Vittorio
dc.contributor.editor
Hebert, Martial
dc.contributor.editor
Sminchisescu, Cristian
dc.contributor.editor
Weiss, Yair
dc.date.accessioned
2018-11-23T07:38:36Z
dc.date.available
2018-11-22T18:06:44Z
dc.date.available
2018-11-23T07:38:36Z
dc.date.issued
2018
dc.identifier.isbn
978-3-030-01260-1
en_US
dc.identifier.isbn
978-3-030-01261-8
en_US
dc.identifier.issn
0302-9743
dc.identifier.issn
1611-3349
dc.identifier.other
10.1007/978-3-030-01261-8_42
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/305722
dc.identifier.doi
10.3929/ethz-b-000305722
dc.description.abstract
This work addresses the problem of semantic scene understanding under dense fog. Although considerable progress has been made in semantic scene understanding, it is mainly related to clear-weather scenes. Extending recognition methods to adverse weather conditions such as fog is crucial for outdoor applications. In this paper, we propose a novel method, named Curriculum Model Adaptation (CMAda), which gradually adapts a semantic segmentation model from light synthetic fog to dense real fog in multiple steps, using both synthetic and real foggy data. In addition, we present three other main stand-alone contributions: 1) a novel method to add synthetic fog to real, clear-weather scenes using semantic input; 2) a new fog density estimator; 3) the Foggy Zurich dataset comprising 3808 real foggy images, with pixel-level semantic annotations for 16 images with dense fog. Our experiments show that 1) our fog simulation slightly outperforms a state-of-the-art competing simulation with respect to the task of semantic foggy scene understanding (SFSU); 2) CMAda improves the performance of state-of-the-art models for SFSU significantly by leveraging unlabeled real foggy data. The datasets and code will be made publicly available.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Springer
en_US
dc.subject
Semantic foggy scene understanding
en_US
dc.subject
Fog simulation
en_US
dc.subject
Synthetic data
en_US
dc.subject
Curriculum model adaptation
en_US
dc.subject
Curriculum learning
en_US
dc.title
Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding
en_US
dc.type
Conference Paper
dc.date.published
2018-10-06
ethz.book.title
Computer Vision – ECCV 2018 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part XIII
en_US
ethz.journal.title
Lecture Notes in Computer Science
ethz.journal.volume
11217
en_US
ethz.journal.abbreviated
LNCS
ethz.pages.start
707
en_US
ethz.pages.end
724
en_US
ethz.size
18 p.
en_US
ethz.version.deposit
acceptedVersion
en_US
ethz.event
15th European Conference on Computer Vision (ECCV 2018)
en_US
ethz.event.location
Munich, Germany
en_US
ethz.event.date
September 8-14, 2018
en_US
ethz.publication.place
Cham
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
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.::01209 - Lehre Inf.technologie u. Elektrotechnik
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
ethz.date.deposited
2018-11-22T18:06:56Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Embargoed
en_US
ethz.date.embargoend
2019-10-06
ethz.rosetta.installDate
2018-11-23T07:39:02Z
ethz.rosetta.lastUpdated
2019-01-03T09:39:47Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.atitle=Model%20Adaptation%20with%20Synthetic%20and%20Real%20Data%20for%20Semantic%20Dense%20Foggy%20Scene%20Understanding&rft.jtitle=Lecture%20Notes%20in%20Computer%20Science&rft.date=2018&rft.volume=11217&rft.spage=707&rft.epage=724&rft.issn=0302-9743&1611-3349&rft.au=Sakaridis,%20Christos&Dai,%20Dengxin&Hecker,%20Simon&Van%20Gool,%20Luc&rft.isbn=978-3-030-01260-1&978-3-030-01261-8&rft.genre=proceeding&rft_id=info:doi/978-3-030-01260-1&info:doi/978-3-030-01261-8&rft.btitle=Computer%20Vision%20%E2%80%93%20ECCV%202018%2015th%20European%20Conference,%20Munich,%20Germany,%20September%208-14,%202018,%20Proceedings,%20Part%20XIII
 Search via SFX

Files in this item

Thumbnail

Publication type

Show simple item record