Update 2025.md

Author: cmihaigabriel

_editions/2025.md

@@ -13,23 +13,6 @@ MediaEval goes beyond other benchmarks and data science challenges in that it al
 scores to also working to achieve deeper understanding about the challenges. For example, characteristics of the data, strengths and weaknesses of particular types of approaches, and observations 
 about the evaluation procedure.
 
-We are happy to announce the tasks that will be offered in 2025. Please watch this website for more detailed task information that will be posted soon.
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-##### Synthetic Images: Advancing detection of generative AI used in real-world online images
-The goal of this challenge is to develop AI models capable of detecting synthetic images and identifying the specific regions in the images that have been manipulated or synthesized. Approaches will be tested on images synthesized with state-of-the-art approaches and collected from real-world settings online.
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-##### MultiSumm: Multimodal summarization of multiple topically related websites
-Participants are provided with multimodal web content from several cities listing food sharing initiatives (FSIs) in each city. For each city, participants are tasked with creating a multimodal summary of the FSI activities in the city which satisfy specified criteria. Evaluation will explore the use of emerging LLMs-based methods in automated assessment of multimodal multi-document summarization.
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-##### NewsImages: Retrieval and generative AI for news thumbnails
-Participants receive a large set of articles (including the headline and article lead) in the English-language from international publishers. We offer two subtasks: retrieving an image for each article from a collection of images that can serve as a thumbnail, or generating an article thumbnail.
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-##### Memorability: Predicting movie and commercial memorability
-The goal of this task is to predict the memorability of media items. For the memorability task, we provide movie excerpts, tasking teams with inferring how memorable videos are based on visual or EEG features, and commercial videos with the purpose of inferring the memorability of videos and the brands present in the videos.
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-##### Medico: VQA (with multimodal explanations) for gastroinstestinal imaging
-The goal is to use Visual Question Answering (VQA) to interpret and answer questions based on gastrointestinal images, aiming to enhance decision support and improve AI-driven medical decision-making. We provide a gastrointestinal dataset containing images and videos with VQA labels and additional metadata.
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 ##### MediaEval 2025 Schedule:
 * Registration for task participation opens: April 2025
 * Development data release: May 2025