Overview of FungiCLEF 2025: Few-shot classification with rare fungi species

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00388421" target="_blank" >RIV/68407700:21230/25:00388421 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/49777513:23520/25:43976596

Výsledek na webu

<a href="https://ceur-ws.org/Vol-4038/paper_233.pdf" target="_blank" >https://ceur-ws.org/Vol-4038/paper_233.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Overview of FungiCLEF 2025: Few-shot classification with rare fungi species

Popis výsledku v původním jazyce

FungiCLEF 2025, the 4th edition of the FungiCLEF challenge, was organized as part of the LifeCLEF and the FGVC workshops. This year’s edition targeted few-shot classification of rare fungi species. Participants were tasked with identifying species from multimodal observations, including images, structured metadata, and environmental data. The data was collected through citizen science and underwent expert-based labeling. Building upon the FungiTastic dataset, FungiCLEF 2025 emphasized real-world constraints such as limited training samples, high intra-class variability, fine-grained inter-class similarities, and distribution shift. The competition attracted 74 teams, with the leading submissions demonstrating significant gains over the provided baselines, showcasing the potential of pretrained vision transformers, contrastive learning, and ensemble techniques. This overview summarizes the challenge setup, dataset, baselines, participant strategies, and key findings, and outlines directions for future work. The winning team achieved a top-5 accuracy of 78.9%, outperforming baselines by over 52%.

Název v anglickém jazyce

Overview of FungiCLEF 2025: Few-shot classification with rare fungi species

Popis výsledku anglicky

FungiCLEF 2025, the 4th edition of the FungiCLEF challenge, was organized as part of the LifeCLEF and the FGVC workshops. This year’s edition targeted few-shot classification of rare fungi species. Participants were tasked with identifying species from multimodal observations, including images, structured metadata, and environmental data. The data was collected through citizen science and underwent expert-based labeling. Building upon the FungiTastic dataset, FungiCLEF 2025 emphasized real-world constraints such as limited training samples, high intra-class variability, fine-grained inter-class similarities, and distribution shift. The competition attracted 74 teams, with the leading submissions demonstrating significant gains over the provided baselines, showcasing the potential of pretrained vision transformers, contrastive learning, and ensemble techniques. This overview summarizes the challenge setup, dataset, baselines, participant strategies, and key findings, and outlines directions for future work. The winning team achieved a top-5 accuracy of 78.9%, outperforming baselines by over 52%.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/SS73020004" target="_blank" >SS73020004: FunDive: Mapování a monitoring diverzity hub se zaměřením na druhovou ochranu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Working Notes of the Conference and Labs of the Evaluation Forum (CLEF 2025)

ISBN

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ISSN

1613-0073

e-ISSN

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Počet stran výsledku

12

Strana od-do

2920-2931

Název nakladatele

CEUR Workshop Proceedings

Místo vydání

Aachen

Místo konání akce

Madrid

Datum konání akce

9. 9. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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