MediaEval Multimedia Benchmarking (@multimediaeval)
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multimediaeval

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  • Stars
    star
    51
  • Global Org. Rank 78,976 (Top 26 %)
  • Registered over 7 years ago
  • Most used languages
    Python
         60.0 %
    HTML
         20.0 %
    TeX
         20.0 %
  • Location <UNKNOWN>
  • Country Total Rank 1,008

Top repositories

1

2019-Emotion-and-Theme-Recognition-in-Music-Task

The goal of this task is to automatically recognize the emotions and themes conveyed in a music recording using machine learning algorithms.
Python
37
star
2

multimediaeval.github.io

This repository holds the code to the https://multimediaeval.github.io/ website. The `master` branch contains only the `_site` folder built with Jekyll due to the use of a non-whitelisted plugin. To edit content, please go to the `gh-page` branch.
HTML
5
star
3

2017-AcousticBrainz-Genre-Task

This task invites participants to predict genre and subgenre of unknown music recordings (songs) given automatically computed features of those recordings. The goal of our task is to understand how genre classification can explore and address the subjective and culturally-dependent nature of genre categories.
TeX
4
star
4

2019-Pixel-Privacy-Task

This task develops image enhancement approaches that project user privacy. Specifically, it is dedicated to creating technology that invisibly changes or visibly enhances images in such a way that it is no longer possible to automatically infer the location at which they were taken.
2
star
5

2021-Medico-Multimedia

Python
2
star
6

2017-Multimedia-Satellite-Task

This task requires participants to retrieve and link multimedia content from social media streams of events (e.g. flooding, fires, land clearing) that can be remotely sensed from satellite imagery. The purpose of this task is to augment events captured by satellite images with social media reports in order to provide a more comprehensive view.
2
star
7

2020-Flood-Related-Multimedia-Task

Python
1
star
8

2018-Pixel-Privacy-Task

This task develops image enhancement approaches that project user privacy. Specifically, it is dedicated to creating technology that invisibly changes or visibly enhances images in such a way that it is no longer possible to automatically infer the location at which they were taken.
1
star