One benchmarking cycle wraps up, and the next ramps up: News from the MediaEval Multimedia Benchmark

MediaEval, the Multimedia Evaluation Benchmark, has offered a yearly set of multimedia challenges since 2010. MediaEval supports the development of algorithms and technologies for analyzing, exploring and accessing information in multimedia data. MediaEval aims to help make multimedia technology a force for good in society and for this reason focuses on tasks with a human or social aspect. Benchmarking contributes in two ways to advancing multimedia research. First, by offering standardized definitions of tasks and evaluation data sets, it makes it possible to fairly compare algorithms and, in this way, track progress. If we can understand which types of algorithms perform better, we can more easily find ways (and the motivation) to improve them. Second, benchmarking helps to direct the attention of the research community, for example, towards new tasks that are based on real-world needs, or towards known problems for which more research is necessary to have a solution that is good enough for a real world application scenario.

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Publication Details

Journal
ACM SIGMultimedia Records
Published
2024-10-01
DOI
https://doi.org/10.1145/3779015.3779018
Primary Topic
Visual Attention and Saliency Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

One benchmarking cycle wraps up, and the next ramps up: News from the MediaEval Multimedia Benchmark

Mihai Gabriel Constantin, Martha Larson, Steven Hicks
ACM SIGMultimedia Records
Visual Attention and Saliency Detection
article

One benchmarking cycle wraps up, and the next ramps up: News from the MediaEval Multimedia Benchmark

Mihai Gabriel Constantin, Martha Larson, Steven Hicks
article en

Abstract

MediaEval, the Multimedia Evaluation Benchmark, has offered a yearly set of multimedia challenges since 2010. MediaEval supports the development of algorithms and technologies for analyzing, exploring and accessing information in multimedia data. MediaEval aims to help make multimedia technology a force for good in society and for this reason focuses on tasks with a human or social aspect. Benchmarking contributes in two ways to advancing multimedia research. First, by offering standardized definitions of tasks and evaluation data sets, it makes it possible to fairly compare algorithms and, in this way, track progress. If we can understand which types of algorithms perform better, we can more easily find ways (and the motivation) to improve them. Second, benchmarking helps to direct the attention of the research community, for example, towards new tasks that are based on real-world needs, or towards known problems for which more research is necessary to have a solution that is good enough for a real world application scenario.

ACM SIGMultimedia RecordsVol. 16(4)
Radboud University Nijmegen (NL), Simula Metropolitan Center for Digital Engineering (NO), Universitatea Națională de Știință și Tehnologie Politehnica București (RO)
Openalex Percentile: Top 56%
Visual Attention and Saliency Detection
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