Connectionists: Call-for-Participation: ImageCLEF 2025 Multimedia Retrieval in CLEF Lab

[Cristian Stanciu]

[Apologies for multiple postings]

ImageCLEF 2025

Multimedia Retrieval in CLEF

http://www.imageclef.org/2025/

*** CALL FOR PARTICIPATION ***

ImageCLEF 2025 is an evaluation campaign that is being organized as part of
the CLEF (Conference and Labs of the Evaluation Forum) labs. The campaign
offers several research tasks that welcome participation from teams around
the world.

The results of the campaign appear in the working notes proceedings,
published by CEUR Workshop Proceedings (CEUR-WS.org) and are presented in
the CLEF conference. Selected contributions among the participants, will be
invited for publication in the following year in the Springer Lecture Notes
in Computer Science (LNCS) together with the annual lab overviews.

Target communities involve (but are not limited to): information retrieval
(text, vision, audio, multimedia, social media, sensor data, etc.), machine
learning, deep learning, data mining, natural language processing, image
and video processing, computer vision, with special attention to the
challenges of 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)

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)

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)

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)

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)

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)

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)

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: May 10, 2025

- Working notes submission: May 30, 2025

- CLEF 2023 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

Cristian Stanciu, Politehnica University of Bucharest, Romania



On behalf of the organizers,

Cristian Stanciu

https://www.aimultimedialab.ro/
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