An overview on Meta-learning approaches for Few-shot Weakly-supervised Segmentation

Autor(es)

Gama, Pedro Henrique Targino ; Oliveira, Hugo ; dos Santos, Jefersson A. ; Cesar Jr, Roberto M.

• Tipo de documento: Artigo Científico
• Fonte: COMPUTERS & GRAPHICS-UK; v. 113, p. 12-pg., 2023-05-28.

Resumo

Semantic segmentation is a difficult task in computer vision that have applications in many scenarios, often as a preprocessing step for a tool. Current solutions are based on Deep Neural Networks, which often require a large amount of data for learning a task. Aiming to alleviate the strenuous data-collecting/annotating labor, research fields have emerged in recent years. One of them is Meta -Learning, which tries to improve the generability of models to learn in a restricted amount of data. In this work, we extend a previous paper conducting a more extensive overview of the still under -explored problem of Few-Shot Weakly-supervised Semantic Segmentation. We refined the previous taxonomy and included the review of additional methods, including Few-Shot Segmentation methods that could be adapted to the weak supervision. The goal is to provide a simple organization of literature and highlight aspects observed in the current moment, and be a starting point to foment research on this problem with applications in areas like medical imaging, remote sensing, video segmentation, and others.& COPY; 2023 Elsevier Ltd. All rights reserved. (AU)

• Processo FAPESP: 15/22308-2 - Representações intermediárias em Ciência Computacional para descoberta de conhecimento
• Beneficiário: Roberto Marcondes Cesar Junior
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 17/50236-1 - Análise espaço-temporal de imagens de ressonância magnética
• Beneficiário: Roberto Marcondes Cesar Junior
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 20/06744-5 - Deep learning e representações intermediárias para análise de imagens pediátricas
• Beneficiário: Hugo Neves de Oliveira
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado