Research Area:  Machine Learning

Abstract:

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.

Keywords:  

Author(s) Name:  Pedro Henrique Targino Gama, Hugo Oliveira, Jefersson A. dos Santos , Roberto M. Cesar Jr

Publisher name:  ScienceDirect

DOI:  https://doi.org/10.1016/j.cag.2023.05.009

Volume Information:  Volume 113, Pages 77-88, (2023)

Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S0097849323000651