Multi-modal Score Fusion and Decision Trees for Explainable Automatic Job Candidate Screening from Video CVs

Basit Öğe Kaydı
dc.authorid0000-0001-6342-428X
dc.authorid0000-0001-6342-428X
dc.authorid0000-0001-7947-5508
dc.authorscopusid36241785000
dc.authorscopusid56565406500
dc.authorscopusid7006556254
dc.authorwosidSalah, Albert Ali/E-5820-2013
dc.authorwosidSalah, Albert Ali/ABH-5561-2020
dc.contributor.authorKaya, Heysem
dc.contributor.authorGürpınar, Furkan
dc.contributor.authorSalah, Albert Ali
dc.date.accessioned2022-05-11T14:15:51Z
dc.date.available2022-05-11T14:15:51Z
dc.date.issued2017
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description30th IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) -- JUL 21-26, 2017 -- Honolulu, HI
dc.description.abstractWe describe an end-to-end system for explainable automatic job candidate screening from video CVs. In this application, audio, face and scene features are first computed from an input video CV, using rich feature sets. These multiple modalities are fed into modality-specific regressors to predict apparent personality traits and a variable that predicts whether the subject will be invited to the interview. The base learners are stacked to an ensemble of decision trees to produce the outputs of the quantitative stage, and a single decision tree, combined with a rule-based algorithm produces interview decision explanations based on the quantitative results. The proposed system in this work ranks first in both quantitative and qualitative stages of the CVPR 2017 ChaLearn Job Candidate Screening Coopetition.
dc.description.sponsorshipIEEE, IEEE Comp Soc, CVF
dc.description.sponsorshipBogazici UniversityBogazici University [BAP 16A01P4]; BAGEP Award of the Science Academy
dc.description.sponsorshipWe thank the ChaLearn organization and other contributors of this challenge. This work is supported by Bogazici University Project BAP 16A01P4 and by the BAGEP Award of the Science Academy.
dc.identifier.doi10.1109/CVPRW.2017.210
dc.identifier.endpage1659
dc.identifier.isbn978-1-5386-0733-6
dc.identifier.issn2160-7508
dc.identifier.scopus2-s2.0-85030213298
dc.identifier.scopusqualityN/A
dc.identifier.startpage1651
dc.identifier.urihttps://doi.org/10.1109/CVPRW.2017.210
dc.identifier.urihttps://hdl.handle.net/20.500.11776/6098
dc.identifier.wosWOS:000426448300203
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorKaya, Heysem
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartof2017 Ieee Conference on Computer Vision and Pattern Recognition Workshops (Cvprw)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.titleMulti-modal Score Fusion and Decision Trees for Explainable Automatic Job Candidate Screening from Video CVs
dc.typeConference Object

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
6098.pdf
Boyut:
426.76 KB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
Çorlu Mühendislik Fakültesi Koleksiyonu