Transductive meta-learning with enhanced feature ensemble for few-shot semantic segmentation

Abstract This paper addresses few-shot semantic segmentation and proposes a novel transductive end-to-end method that overcomes three key problems affecting performance. First, we present a novel ensemble of visual features learned from pretrained classification and semantic segmentation networks wi...

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Bibliographic Details
Main Authors: Amin Karimi, Charalambos Poullis
Format: Article
Language:English
Published: Nature Portfolio 2024-02-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-024-54640-6