Pinned Repositories
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.
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.
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.
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.
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.
2020-Flood-Related-Multimedia-Task
2021-Medico-Multimedia
2022-Medico-Multimedia
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.
MediaEval Multimedia Benchmarking's Repositories
multimediaeval/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.
multimediaeval/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.
multimediaeval/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.
multimediaeval/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.
multimediaeval/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.
multimediaeval/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.
multimediaeval/2021-Medico-Multimedia
multimediaeval/2020-Flood-Related-Multimedia-Task
multimediaeval/2022-Medico-Multimedia