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Using GAMA and H-ATLAS Data to Explore the Cold Dust Properties of Early-Type Galaxies

Autor: Agius, N. K., Sansom, A. E., Popescu, C. C.

Hierarchical galaxy formation models predict the development of elliptical galaxies through a combination of the mergers and interactions of smaller galaxies. We are carrying out a study of Early-Type Galaxies (ETGs) using GAMA multi-wavelength and H

Externí odkaz: http://arxiv.org/abs/1210.7971

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Meta-Learners for Few-Shot Weakly-Supervised Medical Image Segmentation

Autor: Oliveira, Hugo, Gama, Pedro H. T., Bloch, Isabelle, Cesar Jr, Roberto Marcondes

Most uses of Meta-Learning in visual recognition are very often applied to image classification, with a relative lack of works in other tasks {such} as segmentation and detection. We propose a generic Meta-Learning framework for few-shot weakly-super

Externí odkaz: http://arxiv.org/abs/2305.06912

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Weakly Supervised Few-Shot Segmentation Via Meta-Learning

Autor: Gama, Pedro H. T., Oliveira, Hugo, Junior, José Marcato, Santos, Jefersson A. dos

Semantic segmentation is a classic computer vision task with multiple applications, which includes medical and remote sensing image analysis. Despite recent advances with deep-based approaches, labeling samples (pixels) for training models is laborio

Externí odkaz: http://arxiv.org/abs/2109.01693

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Learning to Segment Medical Images from Few-Shot Sparse Labels

Autor: Gama, Pedro H. T., Oliveira, Hugo, Santos, Jefersson A. dos

In this paper, we propose a novel approach for few-shot semantic segmentation with sparse labeled images. We investigate the effectiveness of our method, which is based on the Model-Agnostic Meta-Learning (MAML) algorithm, in the medical scenario, wh

Externí odkaz: http://arxiv.org/abs/2108.05476

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