NewsImages in MediaEval 2025 – Comparing Image Retrieval and Generation for News Articles

The NewsImages challenge in MediaEval 2025 explores matching news articles with fitting thumbnails. Participants receive a large set of English-language articles from international publishers. Given the text of a news article, the goal of the challenge is to (1) retrieve or (2) generate a fitting image. In two subtasks, participating teams create approaches to fully automate the retrieval/generation workflows as well as develop approaches that feature a human-in-the-loop. The overall quality of the image recommendation is evaluated in a crowd-sourced online event where image fit and relevance are assessed. This task overview paper provides details on the challenge setup, the crowdsourced online evaluation, and technical aspects of the group submissions.

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Publication Details

Journal
Universität Zürich, ZORA
Published
2025-10-25
DOI
https://doi.org/10.5167/uzh-291867
Primary Topic
Multimodal Machine Learning Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

NewsImages in MediaEval 2025 – Comparing Image Retrieval and Generation for News Articles

Benjamin Kille, Mehdi Elahi, Lucien Heitz, Luca Rossetto et al.
Universität Zürich, ZORA
Multimodal Machine Learning Applications
article

NewsImages in MediaEval 2025 – Comparing Image Retrieval and Generation for News Articles

Benjamin Kille, Mehdi Elahi, Lucien Heitz, Luca Rossetto, Andreas Lommatzsch, Duc Tien Dang Nguyen
article en

Abstract

The NewsImages challenge in MediaEval 2025 explores matching news articles with fitting thumbnails. Participants receive a large set of English-language articles from international publishers. Given the text of a news article, the goal of the challenge is to (1) retrieve or (2) generate a fitting image. In two subtasks, participating teams create approaches to fully automate the retrieval/generation workflows as well as develop approaches that feature a human-in-the-loop. The overall quality of the image recommendation is evaluated in a crowd-sourced online event where image fit and relevance are assessed. This task overview paper provides details on the challenge setup, the crowdsourced online evaluation, and technical aspects of the group submissions.

Universität Zürich, ZORA
Openalex Percentile: Top 27%
Multimodal Machine Learning Applications
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NewsImages in MediaEval 2025 – Comparing Image Retrieval and Generation for News Articles — Benjamin Kille, Mehdi Elahi, et al. · Universität Zürich, ZORA (2025) | TGRS Research Map | TGRS