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Understanding images and deep learning models

Abstract

A central goal in the field of Computer Vision is image understanding. In general, appearance cues are used to detect components of interest and then spatial and hierarchical relations among these components are used to "describe" the image content at the semantic level of interest. Current deep models have reached a stage of evolution in which they are able to learn and transfer low level features from one domain to another. However, structural information of images such as spatial and hierarchical relations between constituent components are still explicitly modeled using case specific details. This makes models harder to be understood, useful only for few specific applications, and implications on training data preparation effort is still unclear. The aim of this project is the development of a structure-aware-semantics-unaware deep model, with abilities to learn and encode structural information regardless of the semantic level of image components. This should impact model understandability (as structural information would be more explicitly encoded) and training data requirements (as transfer learning would be possible). Theoretical studies, development of visualization strategies and new deep models, and experimentation with respect to diverse computer vision tasks are planned. (AU)

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VEICULO: TITULO (DATA)

Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
NAKAZONO, L.; DE OLIVEIRA, C. MENDES; HIRATA, N. S. T.; JERAM, S.; QUEIROZ, C.; EIKENBERRY, STEPHEN S.; GONZALEZ, A. H.; ABRAMO, R.; OVERZIER, R.; ESPADOTO, M.; MARTINAZZO, A.; SAMPEDRO, L.; HERPICH, F. R.; ALMEIDA-FERNANDES, F.; WERLE, A.; BARBOSA, C. E.; SODRE JR, L.; LIMA, V, E.; BUZZO, M. L.; CORTESI, A.; MENENDEZ-DELMESTRE, K.; AKRAS, S.; ALVAREZ-CANDAL, ALVARO; LOPES, A. R.; TELLES, E.; SCHOENELL, W.; KANAAN, A.; RIBEIRO, T. On the discovery of stars, quasars, and galaxies in the Southern Hemisphere with S-PLUS DR2. Monthly Notices of the Royal Astronomical Society, v. 507, n. 4, p. 5847-5868, NOV 2021. Web of Science Citations: 0.
HIRATA, NINA S. T.; PAPAKOSTAS, GEORGE A. On Machine-Learning Morphological Image Operators. MATHEMATICS, v. 9, n. 16 AUG 2021. Web of Science Citations: 0.
ESPADOTO, MATEUS; MARTINS, RAFAEL M.; KERREN, ANDREAS; HIRATA, NINA S. T.; TELEA, ALEXANDRU C. Toward a Quantitative Survey of Dimension Reduction Techniques. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, v. 27, n. 3, p. 2153-2173, MAR 1 2021. Web of Science Citations: 3.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.