Weakly Supervised Learning through Rank-based Contextual Measures

Autor(es)

Camacho Presotto, Joao Gabriel ; Valem, Lucas Pascotti ; de Sa, Nikolas Gomes ; Guimaraes Pedronette, Daniel Carlos ; Papa, Joao Paulo ; IEEE COMP SOC

• Tipo de documento: Artigo Científico
• Fonte: 2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR); v. N/A, p. 8-pg., 2021-01-01.

Resumo

Machine learning approaches have achieved remarkable advances over the last decades, especially in supervised learning tasks such as classification. Meanwhile, multimedia data and applications experienced an explosive growth, becoming ubiquitous in diverse domains. Due to the huge increase in multimedia data collections and the lack of labeled data in several scenarios, creating methods capable of exploiting the unlabeled data and operating under weakly supervision is imperative. In this work, we propose a rank-based model to exploit contextual information encoded in the unlabeled data in order to perform weakly supervised classification. We employ different rank-based correlation measures for identifying strong similarities relationships and expanding the labeled set in an unsupervised way. Subsequently, the extended labeled set is used by a classifier to achieve better accuracy results. The proposed weakly supervised approach was evaluated on multimedia classification tasks, considering several combinations of rank correlation measures and classifiers. An experimental evaluation was conducted on 4 public image datasets and different features. Very positive gains were achieved in comparison with various semi-supervised and supervised classifiers taken as baselines when considering the same amount of labeled data. (AU)

• Processo FAPESP: 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo?
• Beneficiário: Alexandre Xavier Falcão
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 19/11104-8 - Uma análise comparativa de métricas de correlação de ranqueamento para aprendizado fracamente supervisionado
• Beneficiário: Nikolas Gomes de Sá
• Modalidade de apoio: Bolsas no Brasil - Iniciação Científica
• 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: 19/04754-6 - Aprendizado fracamente supervisionado baseado em métricas de Ranqueamentto
• Beneficiário: João Gabriel Camacho Presotto
• Modalidade de apoio: Bolsas no Brasil - Mestrado
• 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