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The 2021 ImageCLEF Benchmark: Multimedia Retrieval in Medical, Nature, Internet and Social Media Applications

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Advances in Information Retrieval (ECIR 2021)

Abstract

This paper presents the ideas for the 2021 ImageCLEF lab that will be organized as part of the Conference and Labs of the Evaluation Forum—CLEF Labs 2021 in Bucharest, Romania. ImageCLEF is an ongoing evaluation initiative (active since 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2021, the 19th edition of ImageCLEF will organize four main tasks: (i) a Medical task addressing visual question answering, a concept annotation and a tuberculosis classification task, (ii) a Coral task addressing the annotation and localisation of substrates in coral reef images, (iii) a DrawnUI task addressing the creation of websites from either a drawing or a screenshot by detecting the different elements present on the design and a new (iv) Aware task addressing the prediction of real-life consequences of online photo sharing. The strong participation in 2020, despite the COVID pandemic, with over 115 research groups registering and 40 submitting over 295 runs for the tasks shows an important interest in this benchmarking campaign. We expect the new tasks to attract at least as many researchers for 2021.

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Notes

  1. 1.

    https://www.aicrowd.com/.

  2. 2.

    http://clef2021.clef-initiative.eu/.

  3. 3.

    https://ydsyo.app.

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Acknowledgement

Part of this work is supported under the H2020 AI4Media “A European Excellence Centre for Media, Society and Democracy” project, contract \(\#951911\).

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Correspondence to Bogdan Ionescu .

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Ionescu, B. et al. (2021). The 2021 ImageCLEF Benchmark: Multimedia Retrieval in Medical, Nature, Internet and Social Media Applications. In: Hiemstra, D., Moens, MF., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds) Advances in Information Retrieval. ECIR 2021. Lecture Notes in Computer Science(), vol 12657. Springer, Cham. https://doi.org/10.1007/978-3-030-72240-1_72

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  • DOI: https://doi.org/10.1007/978-3-030-72240-1_72

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