Detecting People in Artwork with CNNs

Nicholas Westlake, Peter Hall, Hongping Cai

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

44 Citations (Scopus)
333 Downloads (Pure)


CNNs have massively improved performance in object detection in photographs. However research into object detection in artwork remains limited. We show state-of-the-art performance on a challenging dataset, People-Art, which contains people from photos, cartoons and 41 different artwork movements. We achieve this high performance by fine-tuning a CNN for this task, thus also demonstrating that training CNNs on photos results in overfitting for photos: only the first three or four layers transfer from photos to artwork. Although the CNN’s performance is the highest yet, it remains less than 60 % AP, suggesting further work is needed for the cross-depiction problem.
Original languageEnglish
Title of host publicationComputer Vision – ECCV 2016 Workshops
Subtitle of host publicationAmsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part I
EditorsGang Hua, Hervé Jégou
PublisherSpringer Berlin Heidelberg
Number of pages17
ISBN (Electronic)9783319466040
ISBN (Print)9783319466033
Publication statusPublished - 18 Sept 2016

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


  • CNNs
  • Cross-depiction problem
  • Object recognition


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