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MDRE 2020 : CFP - MMM2020 MDRE Special Session on Multimedia Datasets for Repeatable Experimentation | |||||||||||||||
Link: http://mmdatasets.org/ | |||||||||||||||
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Call For Papers | |||||||||||||||
Call for Papers
Special Session on “Multimedia Datasets for Repeatable Experimentation (MDRE)" MMM 2020 - 26th International Conference on Multimedia Modeling January 5-8, 2020 @ Daejeon, Korea Information retrieval and multimedia content access has a long history of comparative evaluation and many of the advances in the area over the past decade can be attributed to the availability of open datasets that support comparative and repeatable experimentation. Sharing data and code to allow other researchers to replicate research results is needed in the multimedia modeling field and helps to improve the performance of systems and the reproducibility of published papers. The main focus of this (second edition) special session at MMM2020 is on datasets, and researchers within the multimedia community are encouraged to submit papers adhering to any of the following topics of interest: New datasets and their application domains Valuable experiences gained from making datasets Open-source experimental frameworks Authors are asked to provide a paper describing the motivation, design, and usage of the dataset or framework, as well as a brief summary of the experiments performed to date, and are requested to highlight how this work useful to the community. Submissions: Submissions will follow the exact same author guidelines of MMM 2020. The conference proceedings will be published in the series of Lecture Notes in Computer Science (LNCS) by Springer. Submissions must not exceed 12 pages and must conform to the formatting instructions of Springer Verlag, LNCS series, and adhere to the submission schedule. Regular research paper submissions will be peer-reviewed in a double-blind review process; thus, the submissions must be properly anonymised. Each contribution must be associated with one full registration; only one contribution can be associated with each registration. The submission site can be found here: https://easychair.org/my/conference?conf=mmm2020 Regarding the submission of a paper describing a new MM dataset, the authors are encouraged to make it available by providing a public URL for download, as mentioned above, and agree to the link being maintained on the new MDRE dedicated site (http://mmdatasets.org) that archives outputs of the MDRE special sessions. All datasets described must be licensed in such a manner that it can be legally and freely used with all appropriate ethical approvals completed. Authors are encouraged to prepare appropriate and helpful documentation to accompany the dataset submissions, including examples of how it can be used by the community, examples of successful usage and restrictions on usage. There are similar expectations for the open-source frameworks. Why Submit to the MDRE Special Session? - Accepted contributions will be included in the MMM conference proceedings, as part of the Lecture Notes in Computer Science (LNCS) series by Springer. Authors of selected MMM papers will be invited to publish extended versions in a journal special issue. - Accepted contributions will be listed in the new archive of MDRE multimedia datasets and frameworks (http://mmdatasets.org), thereby increasing their visibility. - Authors of accepted contributions will be invited to present their dataset (or framework) as part of the special session at MMM 2020. - MDRE is planned to be an annual special session at the MMM conference series. Details on MMM2020 Submission: http://www.mmm2020.kr/regular_papers.html Dates: Paper submission deadline: July 12, 2019 Notification of acceptance: September 20, 2019 Camera ready papers due: October 18, 2019 MMM 2020: January 5-8, 2020 (special session date to be decided) Session organizers: - Cathal Gurrin (Dublin City University) - Duc-Tien Dang-Nguyen (Dublin City University) - Klaus Schoeffmann (Klagenfurt University) - Björn Þór Jónsson (IT University of Copenhagen) -------------------------------- ######################################################################## |
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