MMArt-ACM 2019 - International Joint Workshop on Multimedia Artworks Analysis and Attractiveness Computing in Multimedia (MMArt-ACM 2019)

Organizers
Wei-Ta Chu (wtchu-AT-ccu.edu.tw), National Chung Cheng University
Norimichi Tsumura (tsumura-AT-faculty.chiba-u.jp), Graduate School of Engineering, Chiba University
Shoji Yamamoto (yamasho-AT-metro-cit.ac.jp), Tokyo Metropolitan College of Industrial Technology
Toshihiko Yamasaki (yamasaki-AT-hal.t.u-tokyo.ac.jp), The University of Tokyo
Description
The MMArt workshop and ACM workshop will solicit original contributions on diverse works covering multimedia artworks analysis and attractiveness computing. This workshop will be run with the following activities:
Oral presentation: High-quality contributions will be presented in the oral sessions. Each submission will be reviewed by at least three reviewers coming from diverse areas around the world.
Academic and industry keynote talks: We will arrange at least one keynote talk, either from the academic perspective or the industry perspective. The academic keynote speech states state-of-the-art research results and proposes open technical questions. Keynote speaker invited from the industry will show research opportunities and challenges, marketing, business models, and copyright issues.
Invited talks from the main conference: Authors having contributions related to multimedia artworks or attractiveness computing at the main conference may be invited to give talks at this workshop. This arrangement not only strengthens the MMArt-ACM workshop but also facilitates highly-interactive communication between the speaker and the audience.
Scope and Topics
Topics include, but are not limited to analysis and applications of the following areas are solicited:
Creation: content synthesis and collaboration; creation of novel artworks or attractive content; connecting real-world art with digital artworks; attractive content creation.
Editing: content authoring, composition, summarization, and presentation; multimodality integration.
Indexing and retrieval: novel features and structure to index multimedia artworks or attractive content; retrieval interface and model; socially-aware analysis.
Methodology: machine learning for multimedia artworks or attractive content; classification and pattern recognition; generic model and heuristics in analysis.
Interaction: interaction on various devices; user in the loop of computation; human factors in artworks or attractiveness.
Evaluation: dataset development; evaluation of systems for multimedia artworks or attractive content; design of user study; limitation of the state-of-the-art.
Novel applications: novel application scenarios; development of novel challenges and perspectives.