Detecting People in Artwork with CNNs
| Autor: | Westlake, Nicholas, Cai, Hongping, Hall, Peter |
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| Rok vydání: | 2016 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1007/978-3-319-46604-0_57 |
| Popis: | 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. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-46604-0_57 Comment: 14 pages, plus 3 pages of references; 7 figures in ECCV 2016 Workshops |
| Databáze: | arXiv |
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