GRAPH CONVOLUTIONAL NETWORKS AND MANIFOLD RANKING FOR MULTIMODAL VIDEO RETRIEVAL

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorde Almeida, Lucas Barbosa-
Autor(es): dc.creatorValem, Lucas Pascotti-
Autor(es): dc.creatorPedronette, Daniel Carlos Guimarães-
Data de aceite: dc.date.accessioned2025-08-21T22:39:07Z-
Data de disponibilização: dc.date.available2025-08-21T22:39:07Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2021-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/ICIP46576.2022.9897911-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/248246-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/248246-
Descrição: dc.descriptionDespite the impressive advances obtained by supervised deep learning approaches on retrieval and classification tasks, how to acquire labeled data for training remains a challenging bottleneck. In this scenario, the need for developing more effective content-based retrieval approaches capable of taking advantage of multimodal information and advances in unsupervised learning becomes imperative. Based on such observations, we propose two novel approaches that combine Graph Convolutional Networks (GCNs) with rank-based manifold learning methods. The GCN models were trained in an unsupervised way, using the Deep Graph Infomax algorithm, and the proposed approaches employ recent rank-based manifold learning methods. Multimodal information is exploited through pre-trained CNNs via transfer learning for extracting audio, image, and video features. The proposed approaches were evaluated on three public action recognition datasets. High-effective results were obtained, reaching relative gains up to +29.44% of MAP compared to baseline approaches without GCNs. The experimental evaluation also considered classical and recent baselines in the literature.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionDepartment of Statistics Applied Mathematics and Computing (DEMAC) São Paulo State University (UNESP)-
Descrição: dc.descriptionDepartment of Statistics Applied Mathematics and Computing (DEMAC) São Paulo State University (UNESP)-
Descrição: dc.descriptionFAPESP: #2018/15597-6-
Descrição: dc.descriptionFAPESP: #2020/03311-0-
Descrição: dc.descriptionFAPESP: #2020/11366-0-
Formato: dc.format2811-2815-
Idioma: dc.languageen-
Relação: dc.relationProceedings - International Conference on Image Processing, ICIP-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectgraph convolutional networks-
Palavras-chave: dc.subjectmanifold learning-
Palavras-chave: dc.subjectrank aggregation-
Palavras-chave: dc.subjectvideo multimodal retrieval-
Título: dc.titleGRAPH CONVOLUTIONAL NETWORKS AND MANIFOLD RANKING FOR MULTIMODAL VIDEO RETRIEVAL-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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