It is our pleasure to present the technical program of the 10th ACM International Conference on Multimedia Retrieval, ACM ICMR 2020, scheduled to take place October 26-29, 2020 in Dublin, Ireland.
ACM ICMR is the premier conference in the field of multimedia retrieval and covers a broad range of related topics, ranging from multimedia content understanding, via content-based indexing, to human-computer interaction issues. The conference attracts both researchers and practitioners from all over the world and provides a great opportunity for discussing leading-edge research in multimedia retrieval and exchanging ideas.
This year, ACM ICMR received 149 submissions to the main conference, with corresponding authors from 31 countries. After a rigorous review process, 81 submissions were accepted for presentation, including 45 regular paper submissions, 22 special session submissions, 8 demonstration submissions, 3 brave new ideas submissions, and 3 doctoral symposium submissions. The detailed statistics are given in the following table.
We recruited 153 technical program committee (TPC) members, who submitted 451 double-blind reviews for the 149 papers (on average, each submission received 3.03 reviews). We would like to thank the TPC members for their hard work. As a result of their great support, we were able to prepare a strong program for ICMR 2020. We congratulate all authors of accepted submissions.
We wish to also thank the many authors who submitted their work to ICMR 2020. Unfortunately, we had to reject many good submissions to the conference, but we hope that the review process has provided authors of those submissions with feedback and guidance for how to proceed.
Proceeding Downloads
Cited By
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Tran L, Nguyen B, Zhou L and Gurrin C (2023). MyEachtra: Event-Based Interactive Lifelog Retrieval System for LSC’23 ICMR '23: International Conference on Multimedia Retrieval, 10.1145/3592573.3593100, 9798400701887, (24-29), Online publication date: 12-Jun-2023.
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Xiong Z, Wen X, Zhao X, Guo H, Zhao C, Wang J, Hu R, Yue Y and Chen S (2022). Two-level iteration method for multi-task learning with task-isolated labels 2021 International Conference on Computer Vision and Pattern Analysis, 10.1117/12.2626861, 9781510651920, (9)