International Joint Workshop on Multimedia Artworks Analysis and Attractiveness Computing in Multimedia 2020 is in conjuction with ICMR 2020, Dublin, Ireland

Oct. 17, 2019: Workshop website online!

Nov. 19, 2019: Important dates updated

Apr. 06, 2020: Accepted papers announced!

Apr. 06, 2020: ICMR is rescheduled to 26-29th October 2020


International Workshop on Multimedia Artworks Analysis (MMArt) and International Workshop on Attractiveness Computing in Multimedia (ACM) are two disjoint workshops that provided highly interactive venues for researchers in multimedia artworks analysis and attractiveness computing. Two MMArts were held in conjunction with ICME 2016 and ICME 2017, and two ACMs were held in conjunction with BigMM 2016 and BigMM 2017. At ICMR 2018, we jointly hosted these two workshops in order to increase the visibility to attendants of both events and the workshop was very succesfull. We decided to follow the last year's successful workshop at ICMR 2020.

We solicit original researchers on both fields. Particularly, MMArt focuses on novel contributions to multimedia research that focus on emerging type of artworks. In addition to conventional art forms like paintings and photos, we will especially encourage contributions that propose new methodologies, novel challenges, and new applications for emerging multimedia artworks such as comics, illustrations, micro films, animation and game, which may be largely available on social media platforms and are associated with user's comments and ratings.

ACM is intended to provide a forum for researchers and engineers to present their latest innovations and share their experiences on all aspects of attractiveness computing in multimedia.

Topics of ACM include, but are not limited to:

  • Creation: content synthesis and collaboration; creation of novel artworks; connecting traditional art with novel art; connecting real-world art with digital artworks.

  • Editing: content authoring, composition, summarization, and presentation; multimodality integration.

  • Indexing and retrieval: novel features and structure to index multimedia artworks; retrieval interface and model; socially-aware analysis.

  • Methodology: machine learning for multimedia artworks; classification and pattern recognition for multimedia artworks; generic model and heuristics in analysis.

  • Interaction: interaction on various devices; user in the loop of computation; human factors in artworks.

  • Evaluation: dataset development; evaluation of systems for multimedia artworks; design of user study; limitation of the state-of-the-art.

  • Novel applications: novel application scenarios; development of novel challenges and perspectives