| Texto completo | |
| Autor(es): |
de Almeida, Lucas Barbosa
;
Valem, Lucas Pascotti
;
Guimaraes Pedronette, Daniel Carlos
;
IEEE
Número total de Autores: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2022 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP; v. N/A, p. 5-pg., 2022-01-01. |
| Resumo | |
Despite 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. (AU) | |
| Processo FAPESP: | 20/11366-0 - Suporte para ambiente computacional e execução de experimentos: aprendizado fracamente supervisionado e fusão de métodos de classificação |
| Beneficiário: | Lucas Pascotti Valem |
| Modalidade de apoio: | Bolsas no Brasil - Programa Capacitação - Treinamento Técnico |
| Processo FAPESP: | 18/15597-6 - Aplicação e investigação de métodos de aprendizado não-supervisionado em tarefas de recuperação e classificação |
| Beneficiário: | Daniel Carlos Guimarães Pedronette |
| Modalidade de apoio: | Auxílio à Pesquisa - Jovens Pesquisadores - Fase 2 |
| Processo FAPESP: | 20/03311-0 - Aprendizado não supervisionado para recuperação multimídia multimodal |
| Beneficiário: | Lucas Barbosa de Almeida |
| Modalidade de apoio: | Bolsas no Brasil - Mestrado |