Detecting people in artwork with CNNs

Nicholas Westlake, Hongping Cai, Peter Hall

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

42 Citations (SciVal)


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 finetuning 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. ECCV 2016.
EditorsGang Hua, Hervé Jégou
PublisherSpringer Verlag
Number of pages17
ISBN (Print)9783319466033
Publication statusPublished - 18 Sept 2016
Event14th European Conference on Computer Vision, ECCV 2016 - Amsterdam, Netherlands
Duration: 8 Oct 201616 Oct 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9913 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference14th European Conference on Computer Vision, ECCV 2016


  • CNNs
  • Cross-depiction problem
  • Object recognition

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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