Self-Supervised Regression for Query Performance Prediction on Image Retrieval

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorValem, Lucas Pascotti-
Autor(es): dc.creatorPereira-Ferrero, Vanessa Helena-
Autor(es): dc.creatorPedronette, Daniel Carlos Guimaraes-
Data de aceite: dc.date.accessioned2025-08-21T22:34:17Z-
Data de disponibilização: dc.date.available2025-08-21T22:34:17Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2022-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/AIKE59827.2023.00023-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/308058-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/308058-
Descrição: dc.descriptionContent-Based Image Retrieval (CBIR) systems are currently a widely used solution for image retrieval tasks with various applications. Despite the advances achieved, one of the central issues is the need for methods capable of handling the scarcity or absence of labeled data. In this scenario, Query Performance Prediction (QPP) approaches represent a successful technique in the effectiveness estimation of retrieval results. In this work, we propose a novel self-supervised framework, named Regression for Query Performance Prediction Framework - RQPPF, which is flexible and can be used with different regression models. Among the contributions, our training relies only on synthetic data and rank-based features. An experimental evaluation was conducted on 4 different retrieval datasets, considering 14 visual features and 11 regression models. The results indicate highly effective predictions and most of them are greater than recent baselines.-
Descrição: dc.descriptionMicrosoft Research-
Descrição: dc.descriptionPetrobras-
Descrição: dc.descriptionDepartment of Statistics Applied Mathematics and Computing (DEMAC) São Paulo State University (UNESP), São Paulo-
Descrição: dc.descriptionDepartment of Statistics Applied Mathematics and Computing (DEMAC) São Paulo State University (UNESP), São Paulo-
Descrição: dc.descriptionPetrobras: 2023/00095-3-
Formato: dc.format95-98-
Idioma: dc.languageen-
Relação: dc.relationProceedings - 2023 IEEE 6th International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2023-
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Palavras-chave: dc.subjectContent-based Image Retrieval-
Palavras-chave: dc.subjectQuery Performance Prediction-
Palavras-chave: dc.subjectSelf-Supervised Learning-
Título: dc.titleSelf-Supervised Regression for Query Performance Prediction on Image Retrieval-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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