Improving Few-Shot Segmentation Using Negative Class Instances
Keywords
Loading...
Authors
Issue Date
2023-07-25
Language
en
Document type
Journal Title
Journal ISSN
Volume Title
Publisher
Title
ISSN
Volume
Issue
Startpage
Endpage
DOI
Abstract
Existing Few-Shot Semantic Segmentation (FSS) networks only rely on training with query
images containing the class of interest, which hinders their performance in scenarios where
the class is absent, compromising the network’s ecological validity. To rectify this, we propose
a novel training loop that incorporates negative instances during training, providing
the networks with exposure to a broader range of class instances. After testing this approach,
our results show that not only did the models trained on our novel training loop
improve in cases where the class of interest was absent from the query image, the models
also improved when the class of interest was present.
Description
Citation
Supervisor
Faculty
Faculteit der Sociale Wetenschappen