TY - JOUR
AU - She, James
AB -  Session: Engagement 2 “ Digital Society & Multimedia Art, Entertainment and Culture MM ™17, October 23-27, 2017, Mountain View, CA, USA DeepArt: Learning Joint Representations of Visual Arts Hui Mao Ming Cheung James She HKUST-NIE Social Media Lab The Hong Kong University of Science and Technology hmaoaa@connect.ust.hk HKUST-NIE Social Media Lab The Hong Kong University of Science and Technology cpming@ust.hk HKUST-NIE Social Media Lab The Hong Kong University of Science and Technology eejames@ust.hk ABSTRACT This paper aims to generate a better representation of visual arts, which plays a key role in visual arts analysis works. Museums and galleries have a large number of artworks in the database, hiring art experts to do analysis works (e.g., classification, annotation) is time consuming and expensive and the analytic results are not stable because the results highly depend on the experiences of art experts. The problem of generating better representation of visual arts is of great interests to us because of its application potentials and interesting research challenges ”both content information and each unique style information within one artwork should be summarized and learned when generating the representation. For example, by studying a vast number of artworks, art experts summary and enhance 
TI - DeepArt: Learning Joint Representations of Visual Arts
DA - 2017-10-23
UR - https://www.deepdyve.com/lp/association-for-computing-machinery/deepart-learning-joint-representations-of-visual-arts-edsFuayckt
DP - DeepDyve
ER -