Interactive video retrieval evaluation at a distance: comparing sixteen interactive video search systems in a remote setting at the 10th Video Browser Showdown.
IF 3.6 3区 计算机科学Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Silvan Heller, Viktor Gsteiger, Werner Bailer, Cathal Gurrin, Björn Þór Jónsson, Jakub Lokoč, Andreas Leibetseder, František Mejzlík, Ladislav Peška, Luca Rossetto, Konstantin Schall, Klaus Schoeffmann, Heiko Schuldt, Florian Spiess, Ly-Duyen Tran, Lucia Vadicamo, Patrik Veselý, Stefanos Vrochidis, Jiaxin Wu
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引用次数: 0
Abstract
The Video Browser Showdown addresses difficult video search challenges through an annual interactive evaluation campaign attracting research teams focusing on interactive video retrieval. The campaign aims to provide insights into the performance of participating interactive video retrieval systems, tested by selected search tasks on large video collections. For the first time in its ten year history, the Video Browser Showdown 2021 was organized in a fully remote setting and hosted a record number of sixteen scoring systems. In this paper, we describe the competition setting, tasks and results and give an overview of state-of-the-art methods used by the competing systems. By looking at query result logs provided by ten systems, we analyze differences in retrieval model performances and browsing times before a correct submission. Through advances in data gathering methodology and tools, we provide a comprehensive analysis of ad-hoc video search tasks, discuss results, task design and methodological challenges. We highlight that almost all top performing systems utilize some sort of joint embedding for text-image retrieval and enable specification of temporal context in queries for known-item search. Whereas a combination of these techniques drive the currently top performing systems, we identify several future challenges for interactive video search engines and the Video Browser Showdown competition itself.
期刊介绍:
Aims and Scope
The International Journal of Multimedia Information Retrieval (IJMIR) is a scholarly archival journal publishing original, peer-reviewed research contributions. Its editorial board strives to present the most important research results in areas within the field of multimedia information retrieval. Core areas include exploration, search, and mining in general collections of multimedia consisting of information from the WWW to scientific imaging to personal archives. Comprehensive review and survey papers that offer up new insights, and lay the foundations for further exploratory and experimental work, are also relevant.
Relevant topics include
Image and video retrieval - theory, algorithms, and systems
Social media interaction and retrieval - collaborative filtering, social voting and ranking
Music and audio retrieval - theory, algorithms, and systems
Scientific and Bio-imaging - MRI, X-ray, ultrasound imaging analysis and retrieval
Semantic learning - visual concept detection, object recognition, and tag learning
Exploration of media archives - browsing, experiential computing
Interfaces - multimedia exploration, visualization, query and retrieval
Multimedia mining - life logs, WWW media mining, pervasive media analysis
Interactive search - interactive learning and relevance feedback in multimedia retrieval
Distributed and high performance media search - efficient and very large scale search
Applications - preserving cultural heritage, 3D graphics models, etc.
Editorial Policies:
We aim for a fast decision time (less than 4 months for the initial decision)
There are no page charges in IJMIR.
Papers are published on line in advance of print publication.
Academic, industrial researchers, and practitioners involved with multimedia search, exploration, and mining will find IJMIR to be an essential source for important results in the field.