@misc {42446,
	title = {ACM Multimedia Grand Challenge on Detecting Cheapfakes},
	year = {2022},
	note = {https://2022.acmmm.org/call-for-grand-challenge-submissions/},
	publisher = {ACM},
	address = {ACM Multimedia Conference (MM)},
	url = {https://detecting-cheapfakes.github.io},
	author = {Aneja, Shivangi and Midoglu, Cise and Dang-Nguyen, Duc-Tien and Khan, Sohail and Riegler, Michael and Halvorsen, P{\r a}l and Bregler, Chris and Adsumilli, Balu}
}
@inproceedings {42546,
	title = {ICDAR{\textquoteright}22: Intelligent Cross-Data Analysis and Retrieval},
	journal = {2022 International Conference on Multimedia Retrieval},
	year = {2022},
	month = {06/2022},
	publisher = {ACM},
	address = {2022, Newark, NJ, USA},
	abstract = {We have witnessed the rise of cross-data against multimodal dataproblems recently. The cross-modal retrieval system uses a textualquery to look for images; the air quality index can be predictedusing lifelogging images; the congestion can be predicted usingweather and tweets data; daily exercises and meals can help topredict the sleeping quality are some examples of this researchdirection. Although vast investigations focusing on multimodal dataanalytics have been developed, few cross-data (e.g., cross-modaldata, cross-domain, cross-platform) research has been carried on.In order to promote intelligent cross-data analytics and retrievalresearch and to bring a smart, sustainable society to human beings,the specific article collection on "Intelligent Cross-Data Analysisand Retrieval" is introduced. This Research Topic welcomes thosewho come from diverse research domains and disciplines such aswell-being, disaster prevention and mitigation, mobility, climatechange, tourism, healthcare, and food computing.},
	author = {Dao, Minh-Son and Riegler, Michael and Dang-Nguyen, Duc-Tien and Gurrin, Cathal and Nakashima, Yuta and Dong, Mianxiong}
}
@inproceedings {28095,
	title = {Dutkat: A Multimedia System for Catching Illegal Catchers in a Privacy-Preserving Manner},
	journal = {Proceedings of the 2021 Workshop on Intelligent Cross-Data Analysis and Retrieval (ICDAR{\textquoteright}21)},
	year = {2021},
	pages = {57-61},
	publisher = {ACM},
	address = {New York, NY, USA},
	abstract = {Fish crime is considered a global and serious problem for a healthy and sustainable development of one of mankind{\textquoteright}s important sources of food. Technological surveillance and control solutions are emerging as remedies to combat criminal activities, but such solutions might also come with impractical and negative side-effects and challenges. In this paper, we present the concept and design of a surveillance system in lieu of current surveillance trends striking a delicate balance between privacy of legal actors while simultaneously capturing evidence-based footage, sensory data, and forensic proofs of illicit activities. Our proposed novel approach is to assist human operators in the 24/7 surveillance loop of remote professional fishing activities with a privacy-preserving Artificial Intelligence (AI) surveillance system operating in the same proximity as the activities being surveyed. The system will primarily be using video surveillance data, but also other sensor data captured on the fishing vessel. Additionally, the system correlates with other sources such as reports from other fish catches in the approximate area and time, etc. Only upon true positive flagging of specific potentially illicit activities by the locally executing AI algorithms, can forensic evidence be accessed from this physical edge, the fishing vessel. Besides a more privacy-preserving solution, our edge-based AI system also benefits from much less data that has to be transferred over unreliable, low-bandwidth satellite-based networks.},
	isbn = {9781450385299},
	doi = {10.1145/346394410.1145/3463944.3469102},
	url = {https://dl.acm.org/doi/proceedings/10.1145/3463944},
	author = {Tor-Arne Schmidt {Nordmo} and Aril B. {Ovesen} and H{\r a}vard D. {Johansen} and Riegler, Michael and Halvorsen, P{\r a}l and Johansen, Dag},
	editor = {Dao, Minh-Son and Dang-Nguyen, Duc-Tien and Riegler, Michael}
}
@misc {aneja2021mmsys21,
	title = {MMSys{\textquoteright}21 Grand Challenge on Detecting Cheapfakes},
	year = {2021},
	publisher = {arXiv},
	author = {Aneja, Shivangi and Midoglu, Cise and Dang-Nguyen, Duc-Tien and Riegler, Michael and Halvorsen, P{\r a}l and Niessner, Matthias and Adsumilli, Balu and Bregler, Chris}
}
@inbook {26709,
	title = {Image Retrieval Evaluation in Specific Domains},
	booktitle = {Information Retrieval Evaluation in a Changing World - Lessons Learned from 20 Years of CLEF},
	volume = {INRE, volume 41},
	year = {2019},
	pages = {275-305},
	publisher = { Springer},
	organization = { Springer},
	abstract = {Image retrieval was, and still is, a hot topic in research. It comes with many challenges that changed over the years with the emergence of more advanced methods for analysis and enormous growth of images created, shared and consumed. This chapter gives an overview of domain-specific image retrieval evaluation approaches, which were part of the ImageCLEF evaluation campaign . Specifically, the robot vision, photo retrieval, scalable image annotation and lifelogging tasks are presented. The ImageCLEF medical activity is described in a separate chapter in this volume. Some of the presented tasks have been available for several years, whereas others are quite new (like lifelogging). This mix of new and old topics has been chosen to give the reader an idea about the development and trends within image retrieval. For each of the tasks, the datasets, participants, techniques used and lessons learned are presented and discussed leading to a comprehensive summary.},
	doi = {https://doi.org/10.1007/978-3-030-22948-1_12},
	author = {Piras, Luca and Caputo, Barbara and Dang-Nguyen, Duc-Tien and Riegler, Michael and Halvorsen, P{\r a}l},
	editor = {Ferro, Nicola and Peters, Carol}
}
@inproceedings {26587,
	title = {Summarizing E-Sports Matches and Tournaments: The Example of Counter-Strike: Global Offensive},
	journal = {International Workshop on Immersive Mixed and Virtual Environment Systems (MMVE)},
	year = {2019},
	publisher = {ACM},
	abstract = {That video and computer games have reached the masses is a well known fact. Furthermore, game streaming and watching other people play video games is another phenomenon that has outgrown its small beginning by far, and game streams, be it live or recorded, are today viewed by millions. E-sports is the result of organized leagues and tournaments in which players can compete in controlled environments and viewers can experience the matches, discuss and criticize, just like in physical sports. \&nbsp;However, as traditional sports, e-sports matches may be long and contain less interesting parts, introducing the challenge of producing well directed summaries and highlights.\&nbsp;In this paper, we describe our efforts to approach the game streaming and e-sports phenomena from a multimedia research point of view. We focus on the challenge of summarizing matches from specific relevant game, Counter-Strike: Global Offensive (CS:GO). We survey related work, describe the rules and structure of the game and identify the main challenges for summarizing e-sports matches. With this contribution, we aim to foster multimedia research in the area of e-sports and game streaming.},
	author = {Lux, Mathias and Halvorsen, P{\r a}l and Dang-Nguyen, Duc-Tien and H{\r a}kon Kvale {Stensland} and Kesavulu, Manoj and Potthast, Martin and Riegler, Michael}
}
@inproceedings {26274,
	title = {GameStory Task at MediaEval 2018},
	journal = {Proceeding of the MediaEval Benchmarking Initiative for Multimedia Evaluation},
	year = {2018},
	month = {10/2018},
	publisher = {CEUR Workshop Proceedings},
	abstract = {That video games have reached the masses is well known. Moreover, game streaming and watching other people play video games is a phenomenon that has outgrown its small beginnings. Game video streams, be it live or recorded, are viewed by millions. E-sports is the result of organized leagues and tournaments in which players can compete in controlled environments and viewers can experience the matches, discuss and criticize, just like in physical sports. In the GameStory task, taking place the first time in 2018, we approach the game streaming and e-sports phenomena from a multimedia research side. We focus on the task of summarizing matches using a specific relevant game,\&nbsp;Counter-Strike: Global Offensive, as a case study. With the help of ZNIPE.tv, we provide a data set of high quality data and meta data from competitive tournaments and aim to foster research in the area of e-sports and game streaming.},
	author = {Lux, Mathias and Riegler, Michael and Dang-Nguyen, Duc-Tien and Larson, Marcus and Potthast, Martin and Halvorsen, P{\r a}l}
}
@inproceedings {26256,
	title = {Medico Multimedia Task at MediaEval 2018},
	journal = {Working Notes Proceedings of the MediaEval 2018 Workshop},
	year = {2018},
	publisher = {CEUR Workshop Proceedings},
	abstract = {The Medico: Multimedia for Medicine Task, running for the second time as part of MediaEval 2018, focuses on detecting abnormalities, diseases, anatomical landmarks and other findings in images captured by medical devices in the gastrointestinal tract. The task is described, including the use case and its challenges, the dataset with ground truth, the required participant runs and the evaluation metrics.},
	author = {Pogorelov, Konstantin and Riegler, Michael and Halvorsen, P{\r a}l and Hicks, Steven and Kristin Ranheim {Randel} and Dang-Nguyen, Duc-Tien and Lux, Mathias and Ostroukhova, Olga and de Lange, Thomas}
}
@inproceedings {26168,
	title = {Overview of ImageCLEF 2018: Challenges, datasets and evaluation},
	journal = {ImageCLEF 2018},
	year = {2018},
	month = {08/2018},
	publisher = { Springer},
	abstract = {This paper presents an overview of the ImageCLEF 2018 evaluation campaign, an event that was organized as part of the CLEF (Conference and Labs of the Evaluation Forum) Labs 2018. ImageCLEF is an ongoing initiative (it started in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval with the aim of providing information access to collections of images in various usage scenarios and domains. In 2018, the 16th edition of ImageCLEF ran three main tasks and a pilot task: (1) a\&nbsp;caption prediction\&nbsp;task that aims at predicting the caption of a figure from the biomedical literature based only on the figure image; (2) a\&nbsp;tuberculosis\&nbsp;task that aims at detecting the tuberculosis type, severity and drug resistance from CT (Computed Tomography) volumes of the lung; (3) a\&nbsp;LifeLog\&nbsp;task (videos, images and other sources) about daily activities understanding and moment retrieval, and (4) a pilot task on\&nbsp;visual question answering\&nbsp;where systems are tasked with answering medical questions. The strong participation, with over 100 research groups registering and 31 submitting results for the tasks, shows an increasing interest in this benchmarking campaign.},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-98932-7_28},
	author = {Ionescu, Bogdan and M{\"u}ller, Henning and Villegas, Mauricio and Alba Garc{\'\i}a Se {de Herrera} and Eickhoff, Carsten and Andrearczyk, Vincent and Yashin Dicente {Cid} and Liauchuk, Vitali and Kovalev, Vassili and Sadid A {Hasan} and Ling, Yan and Farri, Oladimeji and Liu, Joey and Lungren, Matthew and Dang-Nguyen, Duc-Tien and Prias, Luca and Riegler, Michael and Zhou, Liting and Lux, Mathias and Gurrin, Cathal},
	editor = {Bellot, Patrice}
}
@inproceedings {26275,
	title = {Team ORG @ GameStory Task 2018},
	journal = {Proceeding of the MediaEval Benchmarking Initiative for Multimedia Evaluation},
	year = {2018},
	month = {10/2018},
	publisher = {MediaEval},
	abstract = {This paper describes the approach of the organizers{\textquoteright} team for a submission to the GameStory task at MediaEval 2018. Goal of the task is to provide a summary of a \&nbsp;match of Counter Strike: Global Offensive\&nbsp;(CS:GO), a popular e-sports game, that boils down a long game to it{\textquoteright}s most important events and delivers a story on the progress of the match. Our approach was to provide match statistics and overlay them with events and highlights of the game. We focused on ends of critical rounds, i.e. the rounds where one team took the lead over the other one, and kill streaks, where one player eliminated a substantial number of other players in short time.\&nbsp;},
	author = {Lux, Mathias and Riegler, Michael and Dang-Nguyen, Duc-Tien and Larson, Marcus and Potthast, Martin and Halvorsen, P{\r a}l}
}
@inproceedings {26258,
	title = {Transfer learning with prioritized classification and training dataset equalization for medical objects detection},
	journal = {Working Notes Proceedings of the MediaEval 2018 Workshop},
	year = {2018},
	publisher = {CEUR Workshop Proceedings},
	abstract = {This paper presents the method proposed by the organizer team (SIMULA) for MediaEval 2018 Multimedia for Medicine: the Medico Task. We utilized the recent transfer-learning-based image classification methodology and focused on how easy it is to implement multi-class image classifiers in general and how to improve the classification performance without deep neural network model redesign. The goal for this was both to provide a baseline for the Medico task and to show the\&nbsp; performance of out-of-the-box classifiers for the medical use-case scenario.},
	author = {Ostroukhova, Olga and Pogorelov, Konstantin and Riegler, Michael and Dang-Nguyen, Duc-Tien and Halvorsen, P{\r a}l}
}
@inproceedings {25628,
	title = {A comparison of deep learning with global features for gastrointestinal disease detection},
	journal = {MediaEval Benchmark 2017},
	year = {2017},
	month = {09/2017},
	publisher = {CEUR-WS.org},
	address = {Dublin, Ireland},
	author = {Pogorelov, Konstantin and Riegler, Michael and Halvorsen, P{\r a}l and Griwodz, Carsten and de Lange, Thomas and Kristin Ranheim {Randel} and Sigrun Losada {Eskeland} and Dang-Nguyen, Duc-Tien and Olga, Ostroukhova and Lux, Mathias and Spampinato, Concetto},
	editor = {Riegler, Michael}
}
@inproceedings {25433,
	title = {A Holistic Multimedia System for Gastrointestinal Tract Disease Detection},
	journal = {8th annual ACM conference on Multimedia Systems (MMSys)},
	year = {2017},
	month = {06/2017},
	pages = {112-123},
	publisher = {ACM},
	abstract = {Analysis of medical videos for detection of abnormalities and diseases requires both high precision and recall, but also real-time processing for live feedback and scalability for massive screening of entire populations. Existing work on this field does not provide the necessary combination of retrieval accuracy and performance.\&nbsp;In this paper, a multimedia system is presented where the aim is to tackle automatic analysis of videos from the human gastrointestinal (GI) tract. The system includes the whole pipeline from data collection, processing and analysis, to visualization. The system combines filters using machine learning, image recognition and extraction of global and local image features. Furthermore, it is built in a modular way so that it can easily be extended. At the same time, it is developed for efficient processing in order to provide real-time feedback to the doctors. Our experimental evaluation proves that our system has detection and localisation accuracy at least as good as existing systems for polyp detection, it is capable of detecting a wider range of diseases, it can analyze video in real-time, and it has a low resource consumption for scalability.},
	isbn = {978-1-4503-5002-0},
	doi = {10.1145/3083187.3083189},
	url = {http://dl.acm.org/citation.cfm?id=3083189},
	author = {Pogorelov, Konstantin and Sigrun Losadal {Eskeland} and de Lange, Thomas and Griwodz, Carsten and Kristin Ranheim {Randel} and H{\r a}kon Kvale {Stensland} and Dang-Nguyen, Duc-Tien and Spampinato, Concetto and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l},
	editor = {Cesar, Pablo and Hsu, Cheng-hsin}
}
@inproceedings {25269,
	title = {The JORD System - Linking sky and social multimedia data to Natural and Technological Disasters},
	journal = {ACM International Conference on Multimedia Retrieval},
	year = {2017},
	month = {06/2017},
	publisher = {ACM},
	address = {Bucharest},
	isbn = {978-1-4503-4701-3},
	doi = {10.1145/3078971.3079013},
	author = {Ahmad, Kashif and Riegler, Michael and Riaz, Ans and Conci, Nicola and Dang-Nguyen, Duc-Tien and Halvorsen, P{\r a}l},
	editor = {Ionescu, Bogdan and Sebe, Nicu}
}
@inproceedings {25332,
	title = {Kvasir: A Multi-Class Image-Dataset for Computer Aided Gastrointestinal Disease Detection},
	journal = {ACM Multimedia Systems},
	year = {2017},
	month = {07/2017},
	publisher = {ACM},
	address = {Taiwan},
	author = {Pogorelov, Konstantin and Ranheim, Kristin and Griwodz, Carsten and de Lange, Thomas and Sigrun L. {Eskeland} and Johansen, Dag and Peter Thelin {Schmidt} and Spampinato, Concetto and Dang-Nguyen, Duc-Tien and Lux, Mathias and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {25627,
	title = {Multimedia for medicine: the medico Task at mediaEval 2017},
	journal = {MediaEval Benchmark 2017},
	year = {2017},
	month = {09/2017},
	publisher = {CEUR-WS.org},
	address = {Dublin, Ireland},
	author = {Riegler, Michael and Pogorelov, Konstantin and Halvorsen, P{\r a}l and Griwodz, Carsten and de Lange, Thomas and Kristin Ranheim {Randel} and Sigrun Losada {Eskeland} and Dang-Nguyen, Duc-Tien and Lux, Mathias and Spampinato, Concetto},
	editor = {Riegler, Michael}
}
@inproceedings {25468,
	title = {Organizer Team at ImageCLEFlifelog 2017: Baseline Approaches for Lifelog Retrieval and Summarization},
	journal = {CLEF 2017},
	year = {2017},
	publisher = {CEUR Workshop Proceedings},
	url = {http://ceur-ws.org/Vol-1866/},
	author = {Zhou, Liting and Piras, Luca and Riegler, Michael and Boato, Giulia and Dang-Nguyen, Duc-Tien and Gurrin, Cathal},
	editor = {Cappellato, Linda}
}
@article {ionescu2017overview,
	title = {Overview of ImageCLEF 2017: Information extraction from images},
	journal = {Experimental IR Meets Multilinguality, Multimodality, and Interaction.},
	volume = {10456},
	year = {2017},
	pages = {315-337},
	publisher = {Springer International Publishing},
	isbn = {978-3-319-65813-1},
	author = {Ionescu, Bogdan and Villegas, Mauricio and Arenas, Helbert and Boato, Giulia and Dang-Nguyen, Duc-Tien and Cid, Dicente and Eickhoff, Carsten and Garcia Seco {de Herrera} and Gurrin, Cathal and Riegler, Michael},
	editor = {M{\"u}ller, Henning}
}
@inproceedings {dang2017overview,
	title = {Overview of ImageCLEFlifelog 2017: Lifelog Retrieval and Summarization},
	journal = {CLEF 2017},
	year = {2017},
	publisher = {CEUR-WS.org},
	author = {Riegler, Michael and Boato, Giulia and Zhou, Liting and Gurrin, Cathal},
	editor = {Dang-Nguyen, Duc-Tien}
}
@inproceedings {24990,
	title = {Smart Lifelogging: Recognizing Human Activities using PHASOR},
	journal = {Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods},
	year = {2017},
	month = {02/2017},
	pages = {761-768},
	publisher = {Spinger},
	address = {Portugal},
	isbn = {978-989-758-222-6},
	doi = {10.5220/0006320907610768},
	author = {Dang-Nguyen, Duc-Tien and Dao, Minh-Son and Riegler, Michael and Gurrin, Cathal},
	editor = {De Marsico, Maria and Gabriella Sanniti {di Baja} and Fred, Ana}
}
@inproceedings {24541,
	title = {Heimdallr: a dataset for sport analysis},
	journal = {ACM Multimedia System},
	year = {2016},
	month = {05/2016},
	publisher = {ACM},
	abstract = {In this paper, we present Heimdallr, a dataset that aims to serve two different purposes. The first purpose is action recognition and pose estimation, which requires a dataset of annotated sequences of athlete skeletons. We employed a crowdsourcing platform where people around the world were asked to annotate frames and obtained more than 3000 fully annotated frames for 42 different sequences with a variety of poses and actions. The second purpose is an improved understanding of crowdworkers, and for this purpose, we collected over 10000 written feedbacks from 592 crowdworkers. This is valuable information for crowdsourcing researchers who explore algorithms for worker quality assessment. In addition to the complete dataset, we also provide the code for the application that has been used to collect the data as an open source software.},
	doi = {http://dl.acm.org/citation.cfm?doid=2910017.2910621},
	author = {Riegler, Michael and Dang-Nguyen, Duc-Tien and Winther, B{\r a}rd and Griwodz, Carsten and Pogorelov, Konstantin and Halvorsen, P{\r a}l}
}
@inproceedings {24922,
	title = {ImageCLEF 2017 LifeLog task},
	journal = {Challenges in Machine Learning: Gaming and Education (CiML - NIPS 2016 workshop)},
	year = {2016},
	month = {11/2016},
	publisher = {NIPS},
	address = {Barcelona, Spain},
	author = {Dang-Nguyen, Duc-Tien and Piras, Luca and Riegler, Michael and Gurrin, Cathal and Boato, Giulia and Halvorsen, P{\r a}l}
}
@inproceedings {24855,
	title = {Verifying Multimedia Use at MediaEval 2016},
	journal = {MediaEval 2016 Workshop},
	year = {2016},
	month = {10/2016},
	publisher = {CEUR Workshop Proceedings},
	address = {Hilversum, Netherlands},
	abstract = {This paper provides an overview of the Verifying Multimedia Use task that takes places as part of the 2016 MediaEval Benchmark. The task motivates the development of automated techniques for detecting manipulated and misleading use of web multimedia content. Splicing, tampering and reposting videos and images are examples of manipulation that are part of the task definition. For the 2016 edition of the task, a corpus of images/videos and their associated posts is made available, together with labels indicating the appearance of misuse (fake) or not (real) in each case as well as some useful post metadata.},
	author = {Boididou, Christina and Papadopoulos, Symeon and Dang-Nguyen, Duc-Tien and Boato, Giulia and Riegler, Michael and Petlund, Andreas and Kompatsiaris, Yiannis}
}
@inproceedings {23568,
	title = {Verifying Multimedia Use at MediaEval 2015},
	journal = {MediaEval Benchmarking Initiative for Multimedia Evaluation},
	year = {2015},
	month = {09/2015},
	publisher = {CEUR Workshop Proceedings},
	address = {Wurzen, Germany},
	abstract = {This paper provides an overview of the Verifying Multime-dia Use task that takes places as part of the 2015 MediaEvalBenchmark. The task deals with the automatic detection ofmanipulation and misuse of Web multimedia content. Itsaim is to lay the basis for a future generation of tools thatcould assist media professionals in the process of veri ca-tion. Examples of manipulation include the malicious tam-pering of images and videos, e.g., splicing, removal/additionof elements, while other kinds of misuse include the repost-ing of previously captured multimedia content in a di erentcontext (e.g., a new event) claiming that it was capturedthere. For the 2015 edition of the task, we have generatedand make available a large corpus of past real-world casesof images that were distributed through tweets, along withmanually assigned labels regarding their use, i.e. malicious(fake) versus appropriate (real).},
	author = {Boididou, Christina and Andreadou, Katerina and Papadopoulos, Symeon and Dang-Nguyen, Duc-Tien and Boato, Giulia and Riegler, Michael and Kompatsiaris, Yiannis}
}
@inproceedings {2660120,
	title = {A Crowdsourced Data Set of Edited Images Online},
	journal = {CrowdMM {\textquoteright}14: Proceedings of the 2014 International ACM Workshop on Crowdsourcing for Multimedia},
	year = {2014},
	pages = {49{\textendash}52},
	publisher = {ACM},
	address = {New York, NY, USA},
	isbn = {978-1-4503-3128-9},
	doi = {http://doi.acm.org/10.1145/2660114.2660120},
	author = {Conotter, Valentina and Dang-Nguyen, Duc-Tien and Riegler, Michael and Boato, Giulia}
}
