ImageCLEF - The CLEF Cross Language Image Retrieval Track

Sponsors

ImageCLEF aims to provide an evaluation forum for the cross–language annotation and retrieval of images. Motivated by the need to support multilingual users from a global community accessing the ever growing body of visual information, the main goal of ImageCLEF is to support the advancement of the field of visual media analysis, indexing, classification, and retrieval, by developing the necessary infrastructure for the evaluation of visual information retrieval systems operating in both monolingual, cross–language and language-independent contexts. ImageCLEF aims at providing reusable resources for such benchmarking purposes.

ImageCLEF launched in 2003 as part of the Cross Language Evaluation Forum (CLEF) with the goal is to provide support for the evaluation of 1) language-independent methods for the automatic annotation of images with concepts, 2) multimodal information retrieval methods based on the combination of visual and textual features, and 3) multilingual image retrieval methods, so as to compare the effect of retrieval of image annotations and query formulations in several languages.

ImageCLEF has already seen participation from both academic and industry research groups worldwide from various communities including: (visual) information retrieval, cross–lingual information retrieval, computer vision and pattern recognition, medical informatics, human-computer interaction, etc.

More information on past and current ImageCLEF campaigns can be found here:

• ImageCLEF 2020
• ImageCLEF 2019
• ImageCLEF 2018
• ImageCLEF 2017
• ImageCLEF 2016
• ImageCLEF 2015
• LifeCLEF 2015
• ImageCLEF 2014
• LifeCLEF 2014
• ImageCLEF 2013
• ImageCLEF 2012
• ImageCLEF 2011
• ImageCLEF 2010
• ImageCLEF 2009
• ImageCLEF 2008
• ImageCLEF 2007
• ImageCLEF 2006
• ImageCLEF 2005
• ImageCLEF 2004
• ImageCLEF 2003

"Overview of ImageCLEF 2025"
Bogdan Ionescu, National University of Science and Technology Politehnica Bucharest, Romania
Henning Müller, University of Applied Sciences Western Switzerland (HES-SO), Switzerland
Dan-Cristian Stanciu, National University of Science and Technology Politehnica Bucharest, Romania

1. "Overview of the ImageCLEFMedical 2025 GANs Task: Training Data Analysis and Fingerprint Detection" - Alexandra-Georgiana Andrei, Mihai Gabriel Constantin, Mihai Dogariu, Ahmedkhan Radzhabov, Liviu-Daniel Ștefan, Yuri Prokopchuk, Vassili Kovalev, Henning Müller, Bogdan Ionescu
2. "Overview of ImageCLEFmedical 2025 -- Medical Concept Detection and Interpretable Caption Generation" - Hendrik Damm, Tabea M. G. Pakull, Helmut Becker, Benjamin Bracke, Bahadi̇̀r Eryi̇̀lmaz, Louise Bloch, Raphael Brüngel, Cynthia S. Schmidt, Johannes Rückert, Obioma Pelka, Henning Schäfer, Ahmad Idrissi-Yaghir, Asma Ben Abacha, Alba G. Seco de Herrera, Henning Müller, Christoph M. Friedrich
3. "Overview of ImageCLEFmedical 2025 -- Visual Question Answering and Synthetic Image Generation for Gastrointestinal Tract" - Sushant Gautam, Vajira Thambawita, Michael Riegler, Pål Halvorsen, Steven Hicks
4. "Overview of the MEDIQA-MAGIC Task at ImageCLEF 2025: Multimodal And Generative TelemedICine in Dermatology" - Wen-Wai Yim, Asma Ben Abacha, Noel Codella, Roberto Andres Novoa, Josep Malvehy
5. "Overview of the 2025 ImageCLEFtoPicto Task -- Investigating the Generation of Pictogram Sequences from Text and Speech" - Cécile Macaire, Diandra Fabre, Benjamin Lecouteux, Didier Schwab
6. "Overview of ImageCLEF 2025 -- Multimodal Reasoning" - Dimitar Dimitrov, Ming Shan Hee, Zhuohan Xie, Rocktim Jyoti Das, Momina Ahsan, Sarfraz Ahmad, Nikolay Paev, Ivan Koychev, Preslav Nakov
7. "Overview of Image Retrieval/Generation for Arguments" - Johannes Kiesel, GESIS – Leibniz Institute for the Social Sciences

1. "Parlez-vous Picto? A Transformer-Based Approach for Text-to-Picto and Speech-to-Picto Translation in French" - Maja J. Hjuler, Indira Fabre

1. "Reverse Engineering Generative Fingerprints in Medical Images: A Deep Learning Approach to Training Data Attribution" - Sara Nambiar, Isha Shah and Nikita Bhedasgaonkar, Pune Institute of Computer Technology, India
2. "Evaluation of the Privacy of Images Generated by ImageCLEFmedical GANs 2025 Based on Pre-trained Model Feature Extraction Methods" – Dengtao Zhang, YunNan University, China
3. "Evaluating of the Privacy of Images Generated by ImageCLEFmedical GAN 2025 Using Similarity Classification Method Based on Image Enhancement and Deep Learning Model" – Haojie Zuo, YunNan University, China
4. "ViT-based generative model fingerprinting" - Yijiang Zhou, YunNan University, China
5. "Detecting Training Data Usage in Synthetic Images Using Machine Learning Techniques" - Krithikha Sanju S, Sri Sivasubramaniya Nadar College of Engineering, India
6. "Identify Training Data Subsets in GAN-Generated Medical Images" - Shruti Chandrasekar, Vedajanaani R S and Vijayalakshmi P, Sri Sivasubramaniya Nadar College of Engineering, India
7. "Detecting Training Data Fingerprints in GAN-Generated Medical Images" - Shruti Chandrasekar, Vedajanaani R S and Vijayalakshmi P, Sri Sivasubramaniya Nadar College of Engineering, India

1. "AUEB NLP Group/Archimedes at ImageCLEFmedical Caption 2025" - Group AUEB/Archimedes, Greece, Ippokratis Pantelidis/Anna Chatzipapadopoulou
2. "DS4DH Group at ImageCLEFmedical Caption 2025" - Group DS4DH, China/Switzerland/Brasil, Sohrab Ferdowsi
3. "UMUTeam at ImageCLEF 2025: Fine-Tuning a Vision-Language Model for Medical Image Captioning and SapBERT-Based Reranking for Concept Detection" - Group UMUTeam, Spain, Ronghao Pan

1. "Ayesha Amjad at ImageCLEF 2025 Multimodal Reasoning: Visual Question Answering with Structured Data Extraction and Robust Reasoning" - Ayesha Amjad, Fatima Seemab, Saima Kausar, Seemab Latif, Mehwish Fatima
2. "ContextDrift at ImageCLEF 2025 Multimodal Reasoning: Evaluating VLMs' Multimodal, Multilingual and Multidomain Reasoning Capabilities via Thinking Budget Variations and Textual Augmentation" - Vasilena T. Krazheva, Diana Markova, Dimitar I. Dimitrov, Ivan Koychev, Preslav Nakov
3. "MSA at ImageCLEF 2025 Multimodal Reasoning: Multilingual Multimodal Reasoning with Ensemble Vision-Language Models" - Seif Ahmed, Mohamed Younes, Abdelrahman Moustafa, Abdulrahman Allam, Hamza Moustafa

1. "DS@GT at MEDIQA-MAGIC 2025"
2. "IReL, IIT(BHU) at MEDIQA-MAGIC 2025: Tackling Multimodal Dermatology with CLIPSeg-Based Segmentation and BERT-Swin Question Answering" - Krishna Tewari, Abhyudaya Verma, Sukomal Pal

1. "Querying GI Endoscopy Images: A VQA Approach" - Gaurav Parajuli
2. "Multimodal AI for Gastrointestinal Diagnostics: Tackling VQA in MEDVQA-GI 2025" - Sujata Gaihre and Laxmi Tiwari
3. "Towards Better Gastrointestinal Diagnosis: Evaluating Vision-Language Models For GI VQA" - Omar Adjali
4. "Bridging Vision and Language in GI Diagnosis: Florence2 for Question Answering and Stable Diffusion for Image Synthesis" - Krishna Tewari

1. "Reverse Engineering Generative Fingerprints in Medical Images: A Deep Learning Approach to Training Data Attribution" - Sara Nambiar, Isha Shah and Nikita Bhedasgaonkar, Pune Institute of Computer Technology, India

1. "Ayesha Amjad at ImageCLEF 2025 Multimodal Reasoning: Visual Question Answering with Structured Data Extraction and Robust Reasoning" - Fatima Seemab
2. "AUEB NLP Group/Archimedes at ImageCLEFmedical Caption 2025" - Ippokratis Pantelidis/Anna Chatzipapadopoulou
3. "DS4DH at ImageCLEFmedical 2025: Sequence Modeling and Vision-Language Strategies for Medical Concept Detection and Captioning" - Sohrab Ferdowsi
4. "UMUTeam at ImageCLEF 2025: Fine-Tuning a Vision-Language Model for Medical Image Captioning and SapBERT-Based Reranking for Concept Detection" - Ronghao Pan
5. "Modality-Guided Radiology Caption Prediction with small Vision-Language Models and image classifier" - Md Mahmudur Rahman
6. "JJ-VMed: A Framework for Automated Concepts, Captions and Explainability of Medical Image" - Johanna Angulo
7. "AI Stat Lab: A Modular Framework for Clinically Accurate Medical Image Captioning Using Vision-Language Models" - Yunseo Lee
8. "ImageCLEFmedical 2025/2026 – Medical Concept Detection and Interpretable Caption Generation" - Hendrik Damm/Tabea M. G. Pakull

ImageCLEF was originally proposed by Mark Sanderson and Paul Clough from the Department of Information Studies, University of Sheffield. However, today ImageCLEF is organised and ran (mostly voluntarily) by a much larger number of individuals and research groups.

Overall coordination

• Bogdan Ionescu, University Politehnica of Bucharest, Romania, bionescu(at)alpha.imag.pub.ro
• Henning Müller, University of Applied Sciences Western Switzerland in Sierre, Switzerland, henning.mueller(at)hevs.ch

Technical support

• Ivan Eggel, University of Applied Sciences Western Switzerland, Sierre, Switzerland, ivan.eggel(at)hevs.ch

Many older publications related to ImageCLEF are available here.

Many organisations and individuals have supported ImageCLEF, but in particular we thank:

• Dr. Carol Peters (co-ordinator of CLEF) for continual support and help in the organisation of ImageCLEF.
• Dr. Thomas Deselaers for his help in past ImageCLEF editions (2004-2008) and for support of the ImageCLEF web pages.
• The Khresmoi, Chorus+ and Promise projects for their support.