| Texto completo | |
| Autor(es): |
David, Lucas
;
Pedrini, Helio
;
Dias, Zanoni
;
Rocha, Anderson
;
IEEE
Número total de Autores: 5
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2021 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2021); v. N/A, p. 8-pg., 2021-01-01. |
| Resumo | |
Authorship attribution and matching have become paramount activities in current digital art repositories and communities, which seek to efficiently catalog and authenticate the ever-growing number of digitized paintings, uploaded in professional and casual capturing setups, by their own authors or enthusiasts alike. In this work, we employ convolutional network-based strategies to identify and classify art-related digital artifacts over the Painter by Numbers dataset. Firstly, we propose to exploit the authorship, style and genre annotated information in a multi-task setup, in which patches of paintings are encoded through a multiple outputs network and, in a second stage, used in an Siamese discriminating network to solve the authorship matching problem. Secondly, we combine the available annotated information in a more efficient manner, by posing the Painter by Numbers challenge as a multi-label problem. Empirical results show a substantial increase in class-balanced accuracy and ROC AUC score for both multi-task solutions, compared with their simpler counterparts trained using only authorship annotation. Furthermore, a slight increase in ROC AUC score is observed in the multi-label setup, indicating that this simple combination strategy is beneficial to training convergence. (AU) | |
| Processo FAPESP: | 17/12646-3 - Déjà vu: coerência temporal, espacial e de caracterização de dados heterogêneos para análise e interpretação de integridade |
| Beneficiário: | Anderson de Rezende Rocha |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |
| Processo FAPESP: | 15/11937-9 - Investigação de problemas difíceis do ponto de vista algorítmico e estrutural |
| Beneficiário: | Flávio Keidi Miyazawa |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |
| Processo FAPESP: | 17/16246-0 - Análise de mídias sensíveis usando arquiteturas de aprendizado profundo |
| Beneficiário: | Sandra Eliza Fontes de Avila |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |