Publication:
Domain-adaptive self-supervised face & body detection in drawings

dc.contributor.departmentDepartment of Computer Engineering
dc.contributor.departmentGraduate School of Sciences and Engineering
dc.contributor.departmentKUIS AI (Koç University & İş Bank Artificial Intelligence Center)
dc.contributor.kuauthorSezgin, Tevfik Metin
dc.contributor.kuauthorTopal, Barış Batuhan
dc.contributor.kuauthorYüret, Deniz
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.contributor.schoolcollegeinstituteGRADUATE SCHOOL OF SCIENCES AND ENGINEERING
dc.contributor.schoolcollegeinstituteResearch Center
dc.date.accessioned2024-12-29T09:37:55Z
dc.date.issued2023
dc.description.abstractDrawings are powerful means of pictorial abstraction and communication. Understanding diverse forms of drawings, including digital arts, cartoons, and comics, has been a major problem of interest for the computer vision and computer graphics communities. Although there are large amounts of digitized drawings from comic books and cartoons, they contain vast stylistic variations, which necessitate expensive manual labeling for training domain-specific recognizers. In this work, we show how self-supervised learning, based on a teacher-student network with a modified student network update design, can be used to build face and body detectors. Our setup allows exploiting large amounts of unlabeled data from the target domain when labels are provided for only a small subset of it. We further demonstrate that style transfer can be incorporated into our learning pipeline to bootstrap detectors using a vast amount of out-of-domain labeled images from natural images (i.e., images from the real world). Our combined architecture yields detectors with state-of-the-art (SOTA) and near-SOTA performance using minimal annotation effort. Our code can be accessed from https://github.com/barisbatuhan/DASS_Detector.
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.description.sponsorshipFunding text 1: This project is supported by Koc¸ University & ˙s¸ Bank AI Center (KUIS AI). We would like to thank KUIS AI for its support.; Funding text 2: This project is supported by Koç University & Is Bank AI Center (KUIS AI). We would like to thank KUIS AI for its support.
dc.identifier.isbn978-195679203-4
dc.identifier.issn1045-0823
dc.identifier.quartileN/A
dc.identifier.scopus2-s2.0-85170359231
dc.identifier.urihttps://hdl.handle.net/20.500.14288/22513
dc.identifier.wos1202344201058
dc.keywordsArtificial intelligence
dc.keywordsArts computing
dc.keywordsComputer graphics
dc.keywordsFace recognition
dc.language.isoeng
dc.publisherInternational Joint Conferences on Artificial Intelligence
dc.relation.grantnoKUIS
dc.relation.grantnoKoc¸ University & ˙s¸ Bank AI Center
dc.relation.grantnoKoç University & Is Bank AI Center
dc.relation.ispartofIJCAI International Joint Conference on Artificial Intelligence
dc.subjectComputer science, artificial intelligence
dc.subjectComputer science, theory and methods
dc.titleDomain-adaptive self-supervised face & body detection in drawings
dc.typeConference Proceeding
dspace.entity.typePublication
local.contributor.kuauthorTopal, Barış Batuhan
local.contributor.kuauthorYüret, Deniz
local.contributor.kuauthorSezgin, Tevfik Metin
local.publication.orgunit1GRADUATE SCHOOL OF SCIENCES AND ENGINEERING
local.publication.orgunit1College of Engineering
local.publication.orgunit1Research Center
local.publication.orgunit2Department of Computer Engineering
local.publication.orgunit2KUIS AI (Koç University & İş Bank Artificial Intelligence Center)
local.publication.orgunit2Graduate School of Sciences and Engineering
relation.isOrgUnitOfPublication89352e43-bf09-4ef4-82f6-6f9d0174ebae
relation.isOrgUnitOfPublication3fc31c89-e803-4eb1-af6b-6258bc42c3d8
relation.isOrgUnitOfPublication77d67233-829b-4c3a-a28f-bd97ab5c12c7
relation.isOrgUnitOfPublication.latestForDiscovery89352e43-bf09-4ef4-82f6-6f9d0174ebae
relation.isParentOrgUnitOfPublication8e756b23-2d4a-4ce8-b1b3-62c794a8c164
relation.isParentOrgUnitOfPublication434c9663-2b11-4e66-9399-c863e2ebae43
relation.isParentOrgUnitOfPublicationd437580f-9309-4ecb-864a-4af58309d287
relation.isParentOrgUnitOfPublication.latestForDiscovery8e756b23-2d4a-4ce8-b1b3-62c794a8c164

Files