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ImageCLEF 2025 Multimedia Retrieval in CLEF http://www.imageclef.org/2025/
We warmly invite you to take part in this year’s ImageCLEF evaluation campaign! With seven exciting and challenging tasks—each featuring multiple sub-tasks and unique research opportunities—there’s something for everyone. You and your team can begin development immediately, as all the training data is already available. Don’t miss the chance to showcase your skills and secure a spot on our leaderboard!
*** CALL FOR PARTICIPATION *** ImageCLEF 2025 is an evaluation campaign conducted as part of the CLEF (Conference and Labs of the Evaluation Forum) labs. It features multiple research tasks, inviting teams from around the world to participate. The campaign results are published in the working notes proceedings of CEUR Workshop Proceedings (CEUR-WS.org) and presented at the CLEF conference. Additionally, selected contributions from participants may be invited for publication in the following year’s Springer Lecture Notes in Computer Science (LNCS), alongside the annual lab overviews. ImageCLEF’s target communities include, but are not limited to, researchers in information retrieval (text, vision, audio, multimedia, social media, sensor data, etc.), machine learning, deep learning, data mining, natural language processing, image and video processing, and computer vision. The campaign places particular emphasis on challenges related to multi-modality, multi-linguality, and interactive search.
*** 2025 TASKS *** ImageCLEFmedical Automatic Image Captioning ImageCLEFmedical Synthetic Medical Images Created via GANs ImageCLEFmedical Visual Question Answering ImageCLEFmedical Multimodal And Generative TelemedICine (MAGIC) Image Retrieval/Generation for Arguments ImageCLEFtoPicto ImageCLEF Multimodal Reasoning
#ImageCLEFmedical Automatic Image Captioning (9th edition) - Training data released!
https://www.imageclef.org/2025/medical/caption Interpreting and summarizing the insights gained from medical images such as radiology output is a time-consuming task that involves highly trained experts and often represents a bottleneck in clinical diagnosis pipelines.The Automatic Image Captioning task is split into 2 subtasks: Concept Detection Task, based on identifying the presence and location of relevant concepts in a large corpus of medical images and the Caption Prediction Task, where participating systems are tasked with composing coherent captions for the entirety of an image
Organizers: Hendrik Damm, Johannes Rückert, Christoph M. Friedrich, Louise Bloch, Raphael Brüngel, Ahmad Idrissi-Yaghir, Benjamin Bracke (University of Applied Sciences and Arts Dortmund, Germany), Asma Ben Abacha (Microsoft, USA), Alba García Seco de Herrera (University of Essex, UK), Henning Müller (University of Applied Sciences Western Switzerland, Sierre, Switzerland), Henning Schäfer, Tabea M. G. Pakull (Institute for Transfusion Medicine, University Hospital Essen, Germany), Cynthia S. Schmidt, Obioma Pelka (Institute for Artificial Intelligence in Medicine, Germany)
#ImageCLEFmedical Synthetic Medical Images Created via GANs (3rd edition) - Train & Test data released!
https://www.imageclef.org/2025/medical/gan The task aims to further investigate the hypothesis that generative models generate synthetic medical images that retain "fingerprints" from the real images used during their training. These fingerprints raise important security and privacy concerns, particularly in the context of personal medical image data being used to create artificial images for various real-life applications. In the first subtask, participants will analyze synthetic biomedical images to determine whether specific real images were used in the training process of generative models. In the second subtask, participants will link each synthetic biomedical image to the specific subset of real data used during its generation. The goal is to identify the particular dataset of real images that contributed to the training of the generative model responsible for creating each synthetic image.
Organizers: Alexandra Andrei, Liviu-Daniel Ștefan, Mihai Gabriel Constantin, Mihai Dogariu, Bogdan Ionescu (National University of Science and Technology POLITEHNICA Bucharest, Romania), Ahmedkhan Radzhabov, Yuri Prokopchuk (National Academy of Science of Belarus, Minsk, Belarus), Vassili Kovalev (Belarusian Academy of Sciences, Minsk, Belarus), Henning Müller (University of Applied Sciences Western Switzerland, Sierre, Switzerland)
#ImageCLEFmedical Visual Question Answering (3rd edition) - Train & Test data released!
https://www.imageclef.org/2025/medical/vqa This year, the challenge looks at the integration of Visual Question Answering (VQA) with synthetic gastrointestinal (GI) data, aiming to enhance diagnostic accuracy and learning algorithms. The challenge includes developing algorithms that can interpret and answer questions based on synthetic GI images, creating advanced synthetic images that mimic accurate diagnostic visuals in detail and variability, and evaluating the effectiveness of VQA techniques with both synthetic and real GI data. The 1st subtask asks participants to build algorithms that can accurately interpret and respond to questions pertaining to gastrointestinal (GI) images. This involves understanding the context and details within the images and providing precise answers that would assist in medical diagnostics, while the 2nd subtask focuses on the generation of synthetic GI images that are highly detailed and variable enough to closely resemble real medical images.
Organizers: Steven A. Hicks, Sushant Gautam, Michael A. Riegler, Vajira Thambawita, Pål Halvorsen (SimulaMet, Norway)
#ImageCLEFmedical Multimodal And Generative TelemedICine (MEDIQA-MAGIC) (3rd edition) - Train data is released!
https://www.imageclef.org/2025/medical/mediqa The task extends on the previous year’s dataset and challenge based on multimodal dermatology response generation. Participants will be given a clinical narrative context along with accompanying images. The task is divided into two relevant sub-parts: (i) segmentation of dermatological problem regions, and (ii) providing answers to closed-ended questions (participants will be given a dermatological query, its accompanying images, as well as a closed-question with accompanying choices – the task is to select the correct answer to each question)
Organizers: Asma Ben Abacha, Wen-wai Yim, Noel Codella (Microsoft), Roberto Andres Novoa (Stanford University), Josep Malvehy (Hospital Clinic of Barcelona)
#Image Retrieval/Generation for Arguments (4th edition) - In collaboration with Touché!
https://www.imageclef.org/2025/argument-images Given a set of arguments, the task is to return for each argument several images that help convey the argument. A suitable image could depict the argument or show a generalization or specialization. Participants can optionally add a short caption that explains the meaning of the image. Images can be either retrieved from the focused crawl or generated using an image generator.
Organizers: Maximilian Heinrich, Johannes Kiesel, Benno Stein (Bauhaus-Universität Weimar), Moritz Wolter (Leipzig University), Martin Potthast (University of Kassel, hessian.AI, scads.AI)
#ImageCLEFtoPicto (3rd edition) - Train & Test data released!
https://www.imageclef.org/2025/topicto The goal of ToPicto is to bring together linguists, computer scientists, and translators to develop new translation methods to translate either speech or text into a corresponding sequence of pictograms. The task refers to the relationship between text and related pictograms and is composed of 2 subtasks: the Text-to-Picto task, which focuses on the automatic generation of a corresponding sequence of pictogram terms and the Speech-to-Picto task, which focuses on directly translating speech to pictogram terms.
Organizers: Diandra Fabre, Cécile Macaire, Benjamin Lecouteux, Didier Schwab (Université Grenoble Alpes, LIG, France)
#ImageCLEF Multimodal Reasoning (new) - Train data released!
https://www.imageclef.org/2025/multimodalreasoning MultimodalReason is a new task focusing on Multilingual Visual Question Answering (VQA). The formulation of the task is the following: Given an image of a question with 3-5 possible answers, participants must identify the single correct answer.The task is split into many subtasks, each handling a different language (English, Bulgarian, Arabic, Serbian, Italian, Hungarian, Croatian, Urdu, Kazakh, Spanish, with a few more on the way). The task's goal is to assess modern LLMs' reasoning capabilities on complex inputs, presented in different languages, across various subjects.
Organizers: Dimitar Dimitrov, Ivan Koychev (Sofia University "St. Kliment Ohridski", Bulgaria), Rocktim Jyoti Das, Zhuohan Xie, Preslav Nakov (Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE)
*** IMPORTANT DATES *** (may vary depending on the task) - Run submission deadline: May 10, 2025 - Working notes submission: May 30, 2025 - CLEF 2025 conference: September 9-12, 2025, Madrid, Spain
*** REGISTRATION *** Follow the instructions here https://www.imageclef.org/2025
*** OVERALL COORDINATION *** Bogdan Ionescu, Politehnica University of Bucharest, Romania Henning Müller, HES-SO, Sierre, Switzerland Dan-Cristian Stanciu, Politehnica University of Bucharest, Romania
On behalf of the organizers,
Dan-Cristian Stanciu https://www.aimultimedialab.ro/