Publications
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Overview of LifeCLEF location-based species prediction task 2020 (GeoLifeCLEF)
Benjamin Deneu
Titouan Lorieul
Elijah Cole
Maximilien Servajean
Christophe Botella
+2 moreUnderstanding the geographic distribution of species is a key concern in conservation. By pairing species occurrences with environmental features, researchers can model the relationship between an environment and the species which may be found there. To advance the stateof-the-art in this area, a large-scale machine learning competition called GeoLifeCLEF 2020 was organized. It relied on a dataset of 1.9 million species observations paired with high-resolution remote sensing imagery, land cover data, and altitude, in addition to traditional low-resolution climate and soil variables. This paper presents an overview of the competition, synthesizes the approaches used by the participating groups, and analyzes the main results. In particular, we highlight the ability of remote sensing imagery and convolutional neural networks to improve predictive performance, complementary to traditional approaches. -
Overview of the VQA-Med Task at ImageCLEF 2020: Visual Question Answering and Generation in the Medical Domain
Asma Ben Abacha
Vivek V. Datla2
Sadid A. Hasan3
Dina Demner-Fushman1
and Henning M¨uller4
This paper presents an overview of the Medical Visual Question Answering (VQA-Med) task at ImageCLEF 2020. This third edition of VQA-Med included two tasks: (i) Visual Question Answering (VQA), where participants were tasked with answering abnormality questions from the visual content of radiology images and (ii) Visual Question Generation (VQG), consisting of generating relevant questions about radiology images based on their visual content. In VQA-Med 2020, 11 teams participated in at least one of the two tasks and submitted a total of 62 runs. The best team achieved a BLEU score of 0.542 in the VQA task and 0.348 in the VQG task.
Jan 2020
