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|Aff-Wild<ref>{{Cite book|last1=Zafeiriou|first1=S.|last2=Kollias|first2=D.|last3=Nicolaou|first3=M.A.|last4=Papaioannou|first4=A.|last5=Zhao|first5=G.|last6=Kotsia|first6=I.|title=2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |chapter=Aff-Wild: Valence and Arousal 'In-the-Wild' Challenge |date=2017|chapter-url=https://eprints.mdx.ac.uk/22045/1/aff_wild_kotsia.pdf|pages=1980–1987|doi=10.1109/CVPRW.2017.248|isbn=978-1-5386-0733-6|s2cid=3107614|url=http://urn.fi/urn:nbn:fi-fe201902276466 }}</ref><ref>{{Cite journal|last1=Kollias|first1=D.|last2=Tzirakis|first2=P.|last3=Nicolaou|first3=M.A.|last4=Papaioannou|first4=A.|last5=Zhao|first5=G.|last6=Schuller|first6=B.|last7=Kotsia|first7=I.|last8=Zafeiriou|first8=S.|date=2019|title=Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond|url=https://rdcu.be/bmGm2|journal=International Journal of Computer Vision |volume=127|issue=6–7|pages=907–929|doi=10.1007/s11263-019-01158-4|s2cid=13679040|doi-access=free|arxiv=1804.10938}}</ref>
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