Web version October 27-29, 2020 | Opatija, CroatiaFull Submission Deadline: May 1, 2020 EAI is actively monitoring the COVID-19 situation to ensure the safety, comfort and quality of experience for attendees and a successful course of the event. If required, the conference will be postponed (to a later date in 2020) or held inan interactive, live online setting. EAI is committed to holding […]

Web version NEW Conference Dates October 15 – 16, 2020 | Boston, United StatesSubmission Deadline: April 30, 2020 EAI is actively monitoring the COVID-19 situation to ensure the safety, comfort and quality of experience for attendees and a successful course of the event. If required, the conference will be postponed (to a later date in 2020) or held inan interactive, live online […]

Dear colleagues,   as a consequence of the ongoing COVID-19 epidemic, ICL-GNSS 2020 will be organized fully as a virtual conference on the original dates June 2-4. More detailed  information on the arrangements will be posted on the conference website https://events.tuni.fi/icl-gnss2020 later on.   The Industrial/Work-in-Progress track is still open for submissions: -abstract (placeholder) April […]

************************************************************************              Workshop on Robot-based inspection systems and post-processing tools                                          Vienna (Austria), September 8, 2020                                    as part of the Technical Programme of the IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)                                                (https://www.ieee-etfa.org/) ************************************************************************ IMPORTANT DATA: Paper submission: June 5, 2020 (extended) Submission length: 8 pages (double column, IEEE […]

ACM Transactions on Multimedia Computing, Communications and Applications (TOMM) CALL FOR PAPERS Special Issue on Advanced Approaches for Multiple Instance Learning in Multimedia Applications (AMIL)   Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, while the points inside the bags are named instances […]