Prototypical Contrastive Network for Imbalanced Aerial Image Segmentation

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversity of Stirling-
Autor(es): dc.contributorUniversity of Western São Paulo (UNOESTE)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade Federal de Mato Grosso do Sul (UFMS)-
Autor(es): dc.contributorUniversity of Sheffield-
Autor(es): dc.creatorNogueira, Keiller-
Autor(es): dc.creatorFaita-Pinheiro, Mayara Maezano-
Autor(es): dc.creatorMarques Ramos, Ana Paula-
Autor(es): dc.creatorGoncalves, Wesley Nunes-
Autor(es): dc.creatorJunior, José Marcato-
Autor(es): dc.creatorDos Santos, Jefersson A.-
Data de aceite: dc.date.accessioned2025-08-21T22:00:25Z-
Data de disponibilização: dc.date.available2025-08-21T22:00:25Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-01-02-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/WACV57701.2024.00818-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/309836-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/309836-
Descrição: dc.descriptionBinary segmentation is the main task underpinning several remote sensing applications, which are particularly interested in identifying and monitoring a specific category/object. Although extremely important, such a task has several challenges, including huge intra-class variance for the background and data imbalance. Furthermore, most works tackling this task partially or completely ignore one or both of these challenges and their developments. In this paper, we propose a novel method to perform imbalanced binary segmentation of remote sensing images based on deep networks, prototypes, and contrastive loss. The proposed approach allows the model to focus on learning the foreground class while alleviating the class imbalance problem by allowing it to concentrate on the most difficult background examples. The results demonstrate that the proposed method outperforms state-of-the-art techniques for imbalanced binary segmentation of remote sensing images while taking much less training time.-
Descrição: dc.descriptionUniversity of Stirling, Scotland-
Descrição: dc.descriptionUniversity of Western São Paulo (UNOESTE), São Paulo-
Descrição: dc.descriptionSão Paulo State University (UNESP), São Paulo-
Descrição: dc.descriptionFederal University of Mato Grosso Do sul (UFMS), Mato Grosso do Sul-
Descrição: dc.descriptionUniversity of Sheffield, England-
Descrição: dc.descriptionSão Paulo State University (UNESP), São Paulo-
Formato: dc.format8351-8361-
Idioma: dc.languageen-
Relação: dc.relationProceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024-
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Palavras-chave: dc.subjectAlgorithms-
Palavras-chave: dc.subjectand algorithms-
Palavras-chave: dc.subjectApplications-
Palavras-chave: dc.subjectformulations-
Palavras-chave: dc.subjectImage recognition and understanding-
Palavras-chave: dc.subjectMachine learning architectures-
Palavras-chave: dc.subjectRemote Sensing-
Título: dc.titlePrototypical Contrastive Network for Imbalanced Aerial Image Segmentation-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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