Abstract
The ImageCLEF evaluation campaign was integrated with CLEF (Conference and Labs of the Evaluation Forum) for more than 20 years and represents a Multimedia Retrieval challenge aimed at evaluating the technologies for annotation, indexing, and retrieval of multimodal data. Thus, it provides information access to large data collections in usage scenarios and domains such as medicine, argumentation and content recommendation. ImageCLEF 2024 has four main tasks: (i) a Medical task targeting automatic image captioning for radiology images, synthetic medical images created with Generative Adversarial Networks (GANs), Visual Question Answering and medical image generation based on text input, and multimodal dermatology response generation; (ii) a joint ImageCLEF-Touché task Image Retrieval/Generation for Arguments to convey the premise of an argument, (iii) a Recommending task addressing cultural heritage content-recommendation, and (iv) a joint ImageCLEF-ToPicto task aiming to provide a translation in pictograms from natural language. In 2023, participation increased by 67% with respect to 2022 which reveals its impact on the community.
Apart from the general organisers, authors are listed in alphabetical order.
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Acknowledgement
The lab is supported under the H2020 AI4Media “A European Excellence Centre for Media, Society and Democracy” project, contract \(\#951911\). The work of Louise Bloch and Raphael Brüngel was partially funded by a PhD grant from the University of Applied Sciences and Arts Dortmund (FH Dortmund), Germany. The work of Ahmad Idrissi-Yaghir and Henning Schäfer was funded by a PhD grant from the DFG Research Training Group 2535 Knowledge- and data-based personalisation of medicine at the point of care (WisPerMed). Image Retrieval/Generation for Arguments task was partially supported by the European Commission under grant agreement GA 101070014.
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Ionescu, B. et al. (2024). Advancing Multimedia Retrieval in Medical, Social Media and Content Recommendation Applications with ImageCLEF 2024. In: Goharian, N., et al. Advances in Information Retrieval. ECIR 2024. Lecture Notes in Computer Science, vol 14613. Springer, Cham. https://doi.org/10.1007/978-3-031-56072-9_6
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