Detecting People in Artwork with CNNs

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_57Comment: 14 pages, plus 3 pages of references; 7 figures in ECCV 2016 Workshop

Public Service Broadcasting-Friends Groups as a Microcosm of Public Interest Media Advocacy

This article is concerned with the interdependencies between public service broadcasters and the third sector, an area in which there is little research that has provided in-depth analysis of case studies. It investigates and compares three public service broadcasting (PSB)-Friends groups in the UK, Australia, and South Africa. By means of analyzing semi-structured interviews and archival data, we address development, institutionalization and policy impact of the Voice of the Listener & Viewer, ABC-Friends, and SOS Coalition. Drawing on resource-mobilization theory we argue that, in particular, material, human, and informational resources, contextualized with political opportunities, have analytic value in explaining similarities and differences between the groups, which are conceived as a microcosm of public interest media advocacy