Overview and Scope
The Dataset Column (https://records.sigmm.org/open-science/datasets/) of ACM SIGMM Records provides timely updates on the developments in the domain of publicly available multimedia datasets as enabling tools for reproducible research in numerous related areas. It is intended as a platform for further dissemination of useful information on multimedia datasets and studies of datasets covering various domains, published in peer-reviewed journals, conference proceedings, dissertations, or as results of applied research in industry.
The aim of the Dataset Column is therefore not to substitute already established platforms for disseminating multimedia datasets, e.g., Qualinet Databases (https://qualinet.github.io/databases/) , Multimedia Evaluation Benchmark (https://multimediaeval.github.io/), but promote such platforms and particularly interesting datasets and benchmarking challenges associated with them. Multimedia Evaluation Benchmark, MediaEval 2021, registration is now open (https://multimediaeval.github.io). This year’s MediaEval features a wide variety of tasks and datasets tackling a large number of domains, including video privacy, social media data analysis and understanding, news items analysis, medicine and wellbeing, affective and subjective content analysis, and game and sports associated media.
The Column will also continue reporting of contributions presented within Dataset Tracks at relevant conferences, e.g., ACM Multimedia (MM), ACM Multimedia Systems (MMSys), International Conference on Quality of Multimedia Experience (QoMEX), International Conference on Multimedia Modeling (MMM).
Dataset Column in the SIGMM Records
Previously published Dataset Columns are listed below in chronological order.
- Socially significant music events, ACM SIGMM Records September 2013
- Diversity and Credibility for Social Images and Image Retrieval, ACM SIGMM Records, Issue 3, 2017
- Sharing and Reproducibility in ACM SIGMM, ACM SIGMM Records, Issue 2, 2018
- Predicting the Emotional Impact of Movies, ACM SIGMM Records, Issue 4, 2018
- ACM Multimedia 2019 and Reproducibility in Multimedia Research, ACM SIGMM Records, Issue 1, 2019
- The V3C1 Dataset: Advancing the State of the Art in Video Retrieval, ACM SIGMM Records, Issue 2, 2019
- Datasets for Online Multimedia Verification, ACM SIGMM Records, Issue 3, 2019
- Report from the MMM 2019 Special Session on Multimedia Datasets for Repeatable Experimentation (MDRE 2019), ACM SIGMM Records, Issue 3, 2019
- Qualinet Databases: Central Resource for QoE Research – History, Current Status, and Plans, ACM SIGMM Records, Issue 3, 2019
- ToCaDa Dataset with Multi-Viewpoint Synchronized Videos, ACM SIGMM Records, Issue 1, 2020
- MediaEval Multimedia Evaluation Benchmark: Tenth Anniversary and Counting, ACM SIGMM Records, Issue 2, 2020
- Report from the MMM 2020 Special Session on Multimedia Datasets for Repeatable Experimentation (MDRE 2020), ACM SIGMM Records, Issue 3, 2020
Call for Contributions
Those who have created and even previously published elsewhere a dataset, benchmarking initiative or studies of datasets relevant to the multimedia community are very welcome to submit their contribution to the ACM SIGMM Records Dataset Column. Examples of these are the accepted datasets to the open dataset and software track of the ACM MMSys 2021 conference or the datasets presented at QoMEX 2021 conference. Please contact one of the editors responsible for the respective area, Mihai Gabriel Constantin (firstname.lastname@example.org), Karel Fliegel (email@example.com), and Maria Torres Vega (firstname.lastname@example.org) to report your contribution.
Since September 2021, the Dataset Column is edited by Mihai Gabriel Constantin, Karel Fliegel, and Maria Torres Vega. Current editors appreciate the work of the previous team, Martha Larson, Bart Thomee and all other contributors, and will continue and further develop this dissemination platform.
The general scope of the Dataset Column is reviewed above, with the more specific areas of the editors listed below:
- Mihai Gabriel Constantin will be responsible for the datasets related to multimedia analysis, understanding, retrieval and exploration,
- Karel Fliegel for the datasets with subjective annotations related to Quality of Experience (QoE)  research,
- Maria Torres Vega for the datasets related to immersive multimedia systems, networked QoE and cognitive network management.
Mihai Gabriel Constantin is a researcher at the AI Multimedia Lab, University Politehnica of Bucharest, Romania, and got his PhD at the Faculty of Electronics, Telecommunications, and Information Technology at the same university, with the topic “Automatic Analysis of the Visual Impact of Multimedia Data”. He has authored over 25 scientific papers in international conferences and high impact journals, with an emphasis on the prediction of the subjective impact of multimedia items on human viewers and deep ensembles. He participated as researcher in more than 10 research projects, and is a member of program committees and reviewer for several workshops, conferences and journals. He is also an active member of the multimedia processing community, being part of the MediaEval benchmarking initiative organization team, and leading or co-organizing several tasks during MediaEval that include Predicting Media Memorability  and Recommending Movies Using Content , as well as publishing several papers that analyze the data, annotations, participant features, methods, and observed best practices for MediaEval tasks and datasets . More details can be found on his webpage: https://gconstantin.aimultimedialab.ro/.
Karel Fliegel received M.Sc. (Ing.) in 2004 (electrical engineering and audiovisual technology) and his Ph.D. in 2011 (research on modeling of visual perception of image impairment features) both from the Czech Technical University in Prague, Faculty of Electrical Engineering (CTU FEE), Czech Republic. He is an assistant professor at Multimedia Technology Group of CTU FEE. His research interests include multimedia technology, image processing, image and video compression, subjective and objective image quality assessment, Quality of Experience, HVS modeling, and imaging photonics. He has been a member of research teams within various projects especially in the area of visual information processing. He has participated in COST ICT Actions IC1003 Qualinet and IC1105 3D-ConTourNet, responsible for development of Qualinet Databases  (https://qualinet.github.io/databases/) relevant especially to QoE research.
Maria Torres Vega is an FWO (Research Foundation Flanders) Senior Postdoctoral fellow working at the multimedia delivery cluster of the IDLab group of the Ghent University (UGent) currently working on the perception of immersive multimedia applications. She received her M.Sc. degree in Telecommunication Engineering from the Polytechnic University of Madrid, Spain, in 2009. Between 2009 and 2013 she worked as a software and test engineer in Germany with focus on Embedded Systems and Signal Processing. In October 2013, she decided to go back to academia and started her PhD at the Eindhoven University of Technology (Eindhoven, The Netherlands), where she researched on the impact of beam-steered optical wireless networks on the users’ perception of services. This work awarded her PhD in Electrical Engineering in September 2017. In her years in academia (since October 2013), she has authored more than 40 publications, including three best paper awards. Furthermore, she serves as reviewer to a plethora of journals and conferences. In 2020 she served as general chair of the 4th Quality of Experience Management workshop, as tutorial chair of the 2020 Network Softwarization conference (NetSoft), and as demo chair of the Quality of Multimedia Experience conference (QoMex 2020). In 2021, she served as Technical Program Committee (TPC) chair of the 2021 Quality of Multimedia Experience conference (QoMex 2021).
-  Le Callet, P., Möller, S., Perkis, A. Qualinet White Paper on Definitions of Quality of Experience, European Network on Quality of Experience in Multimedia Systems and Services (COST Action IC 1003), Lausanne, Switzerland, Version 1.2, March 2013. (http://www.qualinet.eu/images/stories/QoE_whitepaper_v1.2.pdf)
-  Qualinet Databases: Central Resource for QoE Research – History, Current Status, and Plans, ACM SIGMM Records, Issue 3, 2019 (https://records.sigmm.org/2019/09/06/qualinet-databases-central-resource-for-qoe-research-history-current-status-and-plans/)
-  De Herrera, A. G. S., Kiziltepe, R. S., Chamberlain, J., Constantin, M. G., Demarty, C. H. Faiyaz Doctor, Bogdan Ionescu, and Alan F. Smeaton. Overview of MediaEval 2020 Predicting Media Memorability task: What Makes a Video Memorable. In Working Notes Proceedings of the MediaEval 2020 Workshop. (http://ceur-ws.org/Vol-2882/paper6.pdf)
-  Deldjoo, Y., Constantin, M. G., Dritsas, A., Ionescu, B., Schedl, M. The MediaEval 2018 Movie Recommendation Task: Recommending Movies Using Content. In Working Notes Proceedings of the MediaEval 2018 Workshop. (http://ceur-ws.org/Vol-2283/MediaEval_18_paper_9.pdf)
-  Constantin, M. G., Ştefan, L. D., Ionescu, B., Duong, N. Q., Demarty, C. H., & Sjöberg, M. Visual Interestingness Prediction: A Benchmark Framework and Literature Review. International Journal of Computer Vision, 1-25, 2021. (https://link.springer.com/article/10.1007/s11263-021-01443-1)