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Papers/Detecting People in Artwork with CNNs

Detecting People in Artwork with CNNs

Nicholas Westlake, Hongping Cai, Peter Hall

2016-10-27object-detectionObject Detection
PaperPDFCode(official)

Abstract

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

Results

TaskDatasetMetricValueModel
Object DetectionPeopleArtmAP@0.559Fast R-CNN
3DPeopleArtmAP@0.559Fast R-CNN
2D ClassificationPeopleArtmAP@0.559Fast R-CNN
2D Object DetectionPeopleArtmAP@0.559Fast R-CNN
16kPeopleArtmAP@0.559Fast R-CNN

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