Detecting People in Artwork with CNNs
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In this article, the authors show state-of-the-art performance on a challenging dataset, People-Art, which contains people from photos, cartoons and 41 different artwork movements.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 this http URLread more
Citations
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Cross-Domain Weakly-Supervised Object Detection Through Progressive Domain Adaptation
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Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review
Dhiraj Neupane,Jongwon Seok +1 more
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Discovering Visual Patterns in Art Collections With Spatially-Consistent Feature Learning
TL;DR: The key technical insight is to adapt a standard deep feature to this task by fine-tuning it on the specific art collection using self-supervised learning, and spatial consistency between neighbouring feature matches is used as supervisory fine- Tuning signal.
Posted Content
OmniArt: Multi-task Deep Learning for Artistic Data Analysis.
Gjorgji Strezoski,Marcel Worring +1 more
TL;DR: An efficient and accurate method for multi-task learning with a shared representation applied in the artistic domain and a challenge like nature to the new aggregated data set with almost half a million samples and structured meta-data to encourage further research and societal engagement.
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