CVPR 2024 - FGVC11 Workshop

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F24%3A43973935" target="_blank" >RIV/49777513:23520/24:43973935 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

CVPR 2024 - FGVC11 Workshop

Popis výsledku v původním jazyce

It may be tempting to think that image classification is a solved problem. However, one only needs to look at the poor performance of existing techniques in domains with limited training data and highly similar categories to see that this is not the case. In particular, fine-grained categorization, e.g., the precise differentiation between similar plant or animal species, disease of the retina, architectural styles, etc., is an extremely challenging problem, pushing the limits of both human and machine performance. In these domains, expert knowledge is typically required, and the question that must be addressed is how we can develop artificial systems that can efficiently discriminate between large numbers of highly similar visual concepts.The 11th Workshop on Fine-Grained Visual Categorization (FGVC11) will explore topics related to supervised learning, self-supervised learning, semi-supervised learning, vision and language, matching, localization, domain adaptation, transfer learning, few-shot learning, machine teaching, multimodal learning (e.g., audio and video), 3D-vision, crowd-sourcing, image captioning and generation, out-of-distribution detection, anomaly detection, open-set recognition, human-in-the-loop learning, and taxonomic prediction, all through the lens of fine-grained understanding. Hence, the relevant topics are neither restricted to vision nor categorization.

Název v anglickém jazyce

CVPR 2024 - FGVC11 Workshop

Popis výsledku anglicky

It may be tempting to think that image classification is a solved problem. However, one only needs to look at the poor performance of existing techniques in domains with limited training data and highly similar categories to see that this is not the case. In particular, fine-grained categorization, e.g., the precise differentiation between similar plant or animal species, disease of the retina, architectural styles, etc., is an extremely challenging problem, pushing the limits of both human and machine performance. In these domains, expert knowledge is typically required, and the question that must be addressed is how we can develop artificial systems that can efficiently discriminate between large numbers of highly similar visual concepts.The 11th Workshop on Fine-Grained Visual Categorization (FGVC11) will explore topics related to supervised learning, self-supervised learning, semi-supervised learning, vision and language, matching, localization, domain adaptation, transfer learning, few-shot learning, machine teaching, multimodal learning (e.g., audio and video), 3D-vision, crowd-sourcing, image captioning and generation, out-of-distribution detection, anomaly detection, open-set recognition, human-in-the-loop learning, and taxonomic prediction, all through the lens of fine-grained understanding. Hence, the relevant topics are neither restricted to vision nor categorization.

Druh

W - Uspořádání workshopu

CEP obor

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OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/SS05010008" target="_blank" >SS05010008: Detekce, identifikace a monitoring živočichů pokročilými metodami počítačového vidění</a><br>

Návaznosti

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

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Místo konání akce

Seattle

Stát konání akce

US - Spojené státy americké

Datum zahájení akce

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Datum ukončení akce

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Celkový počet účastníků

100

Počet zahraničních účastníků

99

Typ akce podle státní přísl. účastníků

WRD - Celosvětová akce