@article {27867,
	title = {Kvasir-Capsule, a video capsule endoscopy dataset},
	journal = {Scientific Data},
	volume = {8},
	year = {2021},
	pages = {142},
	publisher = {Springer Nature},
	abstract = {Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology. The potential lies in improving anomaly detection while reducing manual labour. Existing work demonstrates the promising benefits of AI-based computer-assisted diagnosis systems for VCE. They also show great potential for improvements to achieve even better results. Also, medical data is often sparse and unavailable to the research community, and qualified medical personnel rarely have time for the tedious labelling work. We present\&nbsp;Kvasir-Capsule, a large VCE dataset collected from examinations at a Norwegian Hospital.\&nbsp;Kvasir-Capsule\&nbsp;consists of 117 videos which can be used to extract a total of 4,741,504 image frames. We have labelled and medically verified 47,238 frames with a bounding box around findings from 14 different classes. In addition to these labelled images, there are 4,694,266 unlabelled frames included in the dataset. The\&nbsp;Kvasir-Capsule\&nbsp;dataset can play a valuable role in developing better algorithms in order to reach true potential of VCE technology.},
	doi = {10.1038/s41597-021-00920-z},
	url = {http://www.nature.com/articles/s41597-021-00920-z},
	author = {Pia H. {Smedsrud} and Thambawita, Vajira and Hicks, Steven and Gjestang, Henrik and Oda Olsen {Nedrejord} and N{\ae}ss, Espen and Borgli, Hanna and Jha, Debesh and Tor Jan Derek {Berstad} and Sigrun L. {Eskeland} and Lux, Mathias and Espeland, H{\r a}vard and Petlund, Andreas and Duc Tien Dang {Nguyen} and Garcia-Ceja, Enrique and Johansen, Dag and Peter T. {Schmidt} and Toth, Ervin and Hugo Lewi {Hammer} and de Lange, Thomas and Riegler, Michael and Halvorsen, P{\r a}l}
}
@article {27472,
	title = {HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy},
	journal = {Scientific Data},
	volume = {7},
	year = {2020},
	month = {08/2020},
	pages = {1-14},
	publisher = {Springer Nature},
	abstract = {Artificial intelligence is currently a hot topic in medicine. However, medical data is often sparse and hard to obtain due to legal restrictions and lack of medical personnel for the cumbersome and tedious process to manually label training data. These constraints make it difficult to develop systems for automatic analysis, like detecting disease or other lesions. In this respect, this article presents\&nbsp;HyperKvasir, the largest image and video dataset of the gastrointestinal tract available today. The data is collected during real gastro- and colonoscopy examinations at B{\ae}rum Hospital in Norway and partly labeled by experienced gastrointestinal endoscopists. The dataset contains 110,079 images and 374 videos, and represents anatomical landmarks as well as pathological and normal findings. The total number of images and video frames together is around 1 million. Initial experiments demonstrate the potential benefits of artificial intelligence-based computer-assisted diagnosis systems. The\&nbsp;HyperKvasir\&nbsp;dataset can play a valuable role in developing better algorithms and computer-assisted examination systems not only for gastro- and colonoscopy, but also for other fields in medicine.},
	keywords = {dataset, GI, Machine learning},
	doi = {10.1038/s41597-020-00622-y},
	url = {http://www.nature.com/articles/s41597-020-00622-y},
	author = {Borgli, Hanna and Thambawita, Vajira and Pia H. {Smedsrud} and Hicks, Steven and Jha, Debesh and Sigrun L. {Eskeland} and Kristin Ranheim {Randel} and Pogorelov, Konstantin and Lux, Mathias and Duc Tien Dang {Nguyen} and Johansen, Dag and Griwodz, Carsten and H{\r a}kon Kvale {Stensland} and Garcia-Ceja, Enrique and Peter T. {Schmidt} and Hugo Lewi {Hammer} and Riegler, Michael and Halvorsen, P{\r a}l and de Lange, Thomas}
}
@inproceedings {27473,
	title = {ImageCLEF 2020: Multimedia Retrieval in Lifelogging, Medical, Nature, and Internet Applications},
	journal = {European Conference on Information Retrieval},
	year = {2020},
	pages = {533 - 541},
	publisher = {Springer International Publishing},
	address = {Cham},
	abstract = {This paper presents an overview of the 2020 ImageCLEF lab that will be organized as part of the Conference and Labs of the Evaluation Forum{\textemdash}CLEF Labs 2020 in Thessaloniki, Greece. ImageCLEF is an ongoing evaluation initiative (run since 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2020, the 18th edition of ImageCLEF will organize four main tasks: (i) a\&nbsp;Lifelog\&nbsp;task (videos, images and other sources) about daily activity understanding, retrieval and summarization, (ii) a\&nbsp;Medical\&nbsp;task that groups three previous tasks (caption analysis, tuberculosis prediction, and medical visual question answering) with new data and adapted tasks, (iii) a\&nbsp;Coral\&nbsp;task about segmenting and labeling collections of coral images for 3D modeling, and a new (iv)\&nbsp;Web user interface\&nbsp;task addressing the problems of detecting and recognizing hand drawn website UIs (User Interfaces) for generating automatic code. The strong participation, with over 235 research groups registering and 63 submitting over 359 runs for the tasks in 2019 shows an important interest in this benchmarking campaign. We expect the new tasks to attract at least as many researchers for 2020.},
	isbn = {978-3-030-45441-8},
	issn = {0302-9743},
	doi = {10.1007/978-3-030-45442-5_69},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-45442-5_69},
	author = {Ionescu, Bogdan and M{\"u}ller, Henning and P{\'e}teri, Renaud and Dang-Nguyen, Duc-Tien and Zhou, Liting and Piras, Luca and Riegler, Michael and Halvorsen, P{\r a}l and Tran, Minh-Triet and Lux, Mathias and Gurrin, Cathal and Chamberlain, Jon and Clark, Adrian and Campello, Antonio and Alba G. Seco {de Herrera} and Ben Abacha, Asma and Datla, Vivek and Sadid A. {Hasan} and Liu, Joey and Demner-Fushman, Dina and Pelka, Obioma and Christoph M. {Friedrich} and Yashin Dicente {Cid} and Kozlovski, Serge and Liauchuk, Vitali and Kovalev, Vassili and Berari, Raul and Brie, Paul and Fichou, Dimitri and Dogariu, Mihai and Liviu Daniel {Stefan} and Mihai Gabriel {Constantin}},
	editor = {Joemon M. {Jose} and Yilmaz, Emine and Magalh{\~a}es, Jo{\~a}o and Castells, Pablo and Ferro, Nicola and M{\'a}rio J. {Silva} and Martins, Fl{\'a}vio}
}
@inproceedings {leorganiser,
	title = {Organiser Team at ImageCLEFlifelog 2020: A Baseline Approach for Moment Retrieval and Athlete Performance Prediction using Lifelog Data},
	journal = {CLEF2020},
	year = {2020},
	publisher = {CEUR Workshop Proceedings},
	author = {Le, Tu-Khiem and Ninh, Van-Tu and Zhou, Liting and Nguyen-Ngoc, Minh-Huy and Trinh, Huu-Duc and Tran, Nguyen-Hien and Piras, Luca and Riegler, Michael and Halvorsen, P{\r a}l and Lux, Mathias}
}
@inproceedings {ninh2020overview,
	title = {Overview of ImageCLEF lifelog 2020: lifelog moment retrieval and sport performance lifelog},
	journal = {CLEF2020 },
	year = {2020},
	publisher = {CEUR Workshop Proceedings},
	author = {Ninh, Van-Tu and Le, Tu-Khiem and Zhou, Liting and Piras, Luca and Riegler, Michael and Halvorsen, P{\r a}l and Tran, Minh-Triet and Lux, Mathias and Gurrin, Cathal and Dang-Nguyen, Duc-Tien}
}
@inproceedings {27644,
	title = {Overview of the ImageCLEF 2020: Multimedia Retrieval in Medical, Lifelogging, Nature, and Internet Applications},
	journal = {CLEF 2020},
	volume = {12260},
	year = {2020},
	pages = {311 - 341},
	publisher = {Springer International Publishing},
	address = {Cham},
	abstract = {This paper presents an overview of the 2020 ImageCLEF lab that will be organized as part of the Conference and Labs of the Evaluation Forum{\textemdash}CLEF Labs 2020 in Thessaloniki, Greece. ImageCLEF is an ongoing evaluation initiative (run since 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2020, the 18th edition of ImageCLEF will organize four main tasks: (i) a\&nbsp;Lifelog\&nbsp;task (videos, images and other sources) about daily activity understanding, retrieval and summarization, (ii) a\&nbsp;Medical\&nbsp;task that groups three previous tasks (caption analysis, tuberculosis prediction, and medical visual question answering) with new data and adapted tasks, (iii) a\&nbsp;Coral\&nbsp;task about segmenting and labeling collections of coral images for 3D modeling, and a new (iv)\&nbsp;Web user interface\&nbsp;task addressing the problems of detecting and recognizing hand drawn website UIs (User Interfaces) for generating automatic code. The strong participation, with over 235 research groups registering and 63 submitting over 359 runs for the tasks in 2019 shows an important interest in this benchmarking campaign. We expect the new tasks to attract at least as many researchers for 2020.},
	isbn = {978-3-030-58218-0},
	issn = {0302-9743},
	doi = {10.1007/978-3-030-58219-710.1007/978-3-030-58219-7_22},
	url = {https://doi.org/10.1007/978-3-030-58219-7_22},
	author = {Ionescu, Bogdan and M{\"u}ller, Henning and P{\'e}teri, Renaud and Ben Abacha, Asma and Datla, Vivek and Sadid A. {Hasan} and Demner-Fushman, Dina and Kozlovski, Serge and Liauchuk, Vitali and Yashin Dicente {Cid} and Kovalev, Vassili and Pelka, Obioma and Christoph M. {Friedrich} and Alba Garc{\'\i}a Se {de Herrera} and Ninh, Van-Tu and Le, Tu-Khiem and Zhou, Liting and Piras, Luca and Riegler, Michael and Halvorsen, P{\r a}l and Tran, Minh-Triet and Lux, Mathias and Gurrin, Cathal and Dang-Nguyen, Duc-Tien and Chamberlain, Jon and Clark, Adrian and Campello, Antonio and Fichou, Dimitri and Berari, Raul and Brie, Paul and Dogariu, Mihai and Liviu Daniel {Stefan} and Mihai Gabriel {Constantin}},
	editor = {Arampatzis, Avi and Kanoulas, Evangelos and Tsikrika, Theodora and Vrochidis, Stefanos and Joho, Hideo and Lioma, Christina and Eickhoff, Carsten and N{\'e}v{\'e}ol, Aur{\'e}lie and Cappellato, Linda and Ferro, Nicola}
}
@inproceedings {26858,
	title = {ACM Multimedia BioMedia 2019 Grand Challenge Overview},
	journal = {The ACM International Conference on Multimedia (ACM MM)},
	year = {2019},
	month = {10/2019},
	pages = {2563-2567},
	publisher = {ACM Press},
	address = {New York, New York, USA},
	abstract = {The BioMedia 2019 ACM Multimedia Grand Challenge is the first in a series of competitions focusing on the use of multimedia for different medical use-cases. In this year{\textquoteright}s challenge, the participants are asked to develop efficient algorithms which automatically detect a variety of findings commonly identified in the gastrointestinal (GI) tract (a part of the human digestive system). The purpose of this task is to develop methods to aid medical doctors performing routine endoscopy inspections of the GI tract. In this paper, we give a detailed description of the four different tasks of this year{\textquoteright}s challenge, present the datasets used for training and testing, and discuss how each submission is evaluated both qualitatively and quantitatively.},
	isbn = {9781450368896},
	doi = {10.1145/334303110.1145/3343031.3356058},
	url = {http://dl.acm.org/citation.cfm?doid=3343031http://dl.acm.org/citation.cfm?doid=3343031.3356058http://dl.acm.org/ft_gateway.cfm?id=3356058\&ftid=2092095\&dwn=1},
	author = {Hicks, Steven and Petlund, Andreas and de Lange, Thomas and Peter Thelin {Schmidt} and Halvorsen, P{\r a}l and Riegler, Michael and Pia H. {Smedsrud} and Trine B. {Haugen} and Kristin Ranheim {Randel} and Pogorelov, Konstantin and H{\r a}kon Kvale {Stensland} and Dang-Nguyen, Duc-Tien and Lux, Mathias},
	editor = {Larson, Martha and Gravier, Guillaume and Hung, Hayley and Ngo, Chong-Wah and Wei Tsang {Ooi}}
}
@inbook {27154,
	title = {Challenges for Multimedia Research in E-Sports Using Counter-Strike Global Offensive as an Example},
	booktitle = {Savegame},
	volume = {4},
	year = {2019},
	pages = {197 - 206},
	publisher = {Springer Fachmedien Wiesbaden},
	organization = {Springer Fachmedien Wiesbaden},
	address = {Wiesbaden},
	abstract = {That video and computer games have reached the masses is a well-known fact. However, game streaming and, therefore, watching other people play videogames has also outgrown its humble beginnings by far. Game streams, be it live or recorded, are viewed by millions. Many of the streams are broadcasting competitive multiplayer games. This is called e-sports and it is very similar to sports broadcasting. E-sports is organized in 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 this paper, we look into the challenges for computer science in general and multimedia research in particular. The multimedia research community has done a lot of work on video streaming, broadcasting and analyzing the audience, but has missed the opportunity to investigate e-sports in detail. We focus on one particular game we deem representative for e-sports,\&nbsp;Counter-Strike: Global Offensive, and investigate how the audience consumes game streams from competitive tournaments.},
	isbn = {978-3-658-27394-1},
	issn = {2524-3241},
	doi = {10.1007/978-3-658-27395-8_13},
	url = {http://link.springer.com/10.1007/978-3-658-27395-8_13},
	author = {Lux, Mathias and Riegler, Michael and Halvorsen, P{\r a}l and Dang-Nguyen, Duc-Tien and Potthast, Martin},
	editor = {Elmenreich, Wilfried and Ren{\'e} Reinhold {Schallegger} and Schniz, Felix and Gabriel, Sonja and P{\"o}lsterl, Gerhard and Wolfgang B. {Ruge}}
}
@inproceedings {ionescu2019imageclef,
	title = {ImageCLEF 2019: Multimedia Retrieval in Lifelogging, Medical, Nature, and Security Applications},
	journal = {European Conference on Information Retrieval},
	year = {2019},
	month = {02/2019},
	pages = {301{\textendash}308},
	publisher = {Springer},
	author = {Ionescu, Bogdan and M{\"u}ller, Henning and P{\'e}teri, Renaud and Dang-Nguyen, Duc-Tien and Piras, Luca and Riegler, Michael and Tran, Minh-Triet and Lux, Mathias and Gurrin, Cathal and Yashin Dicente {Cid}}
}
@inproceedings {ninh2019lifer,
	title = {LIFER 2.0: Discovering Personal Lifelog Insights using an Interactive Lifelog Retrieval System},
	journal = {CLEF2019 Working Notes},
	year = {2019},
	publisher = {CEUR Workshop Proceedings (CEUR-WS.org)},
	author = {Ninh, Van-Tu and Le, Tu-Khiem and Zhou, Liting and Piras, Luca and Riegler, Michael and Lux, Mathias and Tran, Minh-Triet and Gurrin, Cathal and Dang-Nguyen, Duc-Tien}
}
@inproceedings {dang2019overview,
	title = {Overview of ImageCLEFlifelog 2019: solve my life puzzle and lifelog moment retrieval},
	journal = {CLEF2019 Working Notes},
	volume = {2380},
	year = {2019},
	pages = {09{\textendash}12},
	publisher = {CEUR Workshop Proceedings (CEUR-WS.org)},
	author = {Dang-Nguyen, Duc-Tien and Piras, Luca and Riegler, Michael and Tran, Minh-Triet and Zhou, Liting and Lux, Mathias and Le, Tu-Khiem and Ninh, Van-Tu and Gurrin, Cathal}
}
@inproceedings {dang2018overview,
	title = {Overview of ImageCLEFlifelog 2018: daily living understanding and lifelog moment retrieval},
	journal = {Working Notes of CLEF 2018 - Conference and Labs of the Evaluation Forum},
	year = {2018},
	month = {09/2018},
	publisher = {CEUR Workshop Proceedings},
	address = {Avignon, France},
	url = {http://ceur-ws.org/Vol-2125/},
	author = {Dang-Nguyen, Duc-Tien and Piras, Luca and Riegler, Michael and Zhou, Liting and Lux, Mathias and Gurrin, Cathal},
	editor = {Cappellato, Linda}
}
@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}
}
