%0 Conference Paper %1 10.1007/978-3-031-56072-9_6 %A Ionescu, Bogdan %A Müller, Henning %A Dragulinescu, Ana Maria %A Idrissi-Yaghir, Ahmad %A Radzhabov, Ahmedkhan %A Herrera, Alba Garcia Seco de %A Andrei, Alexandra %A Stan, Alexandru %A Stor\aas, Andrea M. %A Abacha, Asma Ben %A Lecouteux, Benjamin %A Stein, Benno %A Macaire, Cécile %A Friedrich, Christoph M. %A Schmidt, Cynthia Sabrina %A Schwab, Didier %A Esperanca-Rodier, Emmanuelle %A Ioannidis, George %A Adams, Griffin %A Schäfer, Henning %A Manguinhas, Hugo %A Coman, Ioan %A Schöler, Johanna %A Kiesel, Johannes %A Rückert, Johannes %A Bloch, Louise %A Potthast, Martin %A Heinrich, Maximilian %A Yetisgen, Meliha %A Riegler, Michael A. %A Snider, Neal %A Halvorsen, P\aal %A Brüngel, Raphael %A Hicks, Steven A. %A Thambawita, Vajira %A Kovalev, Vassili %A Prokopchuk, Yuri %A Yim, Wen-Wai %B Advances in Information Retrieval %C Cham %D 2024 %E Goharian, Nazli %E Tonellotto, Nicola %E He, Yulan %E Lipani, Aldo %E McDonald, Graham %E Macdonald, Craig %E Ounis, Iadh %I Springer Nature Switzerland %K %P 44--52 %T Advancing Multimedia Retrieval in Medical, Social Media and Content Recommendation Applications with ImageCLEF 2024 %X 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. %@ 978-3-031-56072-9

@inproceedings{10.1007/978-3-031-56072-9_6, 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-Touch{\'e} 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.}, added-at = {2024-09-10T10:41:24.000+0200}, address = {Cham}, author = {Ionescu, Bogdan and M{\"u}ller, Henning and Dr{\u{a}}gulinescu, Ana Maria and Idrissi-Yaghir, Ahmad and Radzhabov, Ahmedkhan and Herrera, Alba Garcia Seco de and Andrei, Alexandra and Stan, Alexandru and Stor{\aa}s, Andrea M. and Abacha, Asma Ben and Lecouteux, Benjamin and Stein, Benno and Macaire, C{\'e}cile and Friedrich, Christoph M. and Schmidt, Cynthia Sabrina and Schwab, Didier and Esperan{\c{c}}a-Rodier, Emmanuelle and Ioannidis, George and Adams, Griffin and Sch{\"a}fer, Henning and Manguinhas, Hugo and Coman, Ioan and Sch{\"o}ler, Johanna and Kiesel, Johannes and R{\"u}ckert, Johannes and Bloch, Louise and Potthast, Martin and Heinrich, Maximilian and Yetisgen, Meliha and Riegler, Michael A. and Snider, Neal and Halvorsen, P{\aa}l and Br{\"u}ngel, Raphael and Hicks, Steven A. and Thambawita, Vajira and Kovalev, Vassili and Prokopchuk, Yuri and Yim, Wen-Wai}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/20ae0ce275bc15d6b4bfae208dab822dd/scadsfct}, booktitle = {Advances in Information Retrieval}, editor = {Goharian, Nazli and Tonellotto, Nicola and He, Yulan and Lipani, Aldo and McDonald, Graham and Macdonald, Craig and Ounis, Iadh}, interhash = {6c2bcea667ec53748bb9df74b1f33cc6}, intrahash = {0ae0ce275bc15d6b4bfae208dab822dd}, isbn = {978-3-031-56072-9}, keywords = {}, pages = {44--52}, publisher = {Springer Nature Switzerland}, timestamp = {2024-09-10T10:47:32.000+0200}, title = {Advancing Multimedia Retrieval in Medical, Social Media and Content Recommendation Applications with ImageCLEF 2024}, year = 2024 }