Zitat
B. Ionescu, H. Müller, A. M. Dragulinescu, A. Idrissi-Yaghir, A. Radzhabov, A. G. S. de Herrera, A. Andrei, A. Stan, A. M. Storås, A. B. Abacha, B. Lecouteux, B. Stein, C. Macaire, C. M. Friedrich, C. S. Schmidt, D. Schwab, E. Esperanca-Rodier, G. Ioannidis, G. Adams, H. Schäfer, H. Manguinhas, I. Coman, J. Schöler, J. Kiesel, J. Rückert, L. Bloch, M. Potthast, M. Heinrich, M. Yetisgen, M. A. Riegler, N. Snider, P. Halvorsen, R. Brüngel, S. A. Hicks, V. Thambawita, V. Kovalev, Y. Prokopchuk, and W.-W. Yim, “Advancing Multimedia Retrieval in Medical, Social Media and Content Recommendation Applications with ImageCLEF 2024,” in Advances in Information Retrieval, 2024, pp. 44–52.
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.