Self-supervised Few-shot Learning for Semantic Segmentation: An
  Annotation-free Approach
Sanaz Karimijafarbigloo Reza Azad Dorit Merhof
Few-shot semantic segmentation (FSS) offers immense potential in the field of
medical image analysis, enabling accurate object segmentation with limited
training data. However, existing FSS techniques heavily rely on annotated
semantic classes, rendering them unsuitable for medical images due to the
scarcity of annotations. To address this challenge, multiple contributions are
proposed: First, inspired by spectral decomposition methods, the problem of
image decomposition is reframed as a graph partitioning task. The eigenvectors
of the Laplacian matrix, derived from the feature affinity matrix of
self-supervised networks, are analyzed to estimate the distribution of the
objects of interest from the support images. Secondly, we propose a novel
self-supervised FSS framework that does not rely on any annotation. Instead, it
adaptively estimates the query mask by leveraging the eigenvectors obtained
from the support images. This approach eliminates the need for manual
annotation, making it particularly suitable for medical images with limited
annotated data. Thirdly, to further enhance the decoding of the query image
based on the information provided by the support image, we introduce a
multi-scale large kernel attention module. By selectively emphasizing relevant
features and details, this module improves the segmentation process and
contributes to better object delineation. Evaluations on both natural and
medical image datasets demonstrate the efficiency and effectiveness of our
method. Moreover, the proposed approach is characterized by its generality and
model-agnostic nature, allowing for seamless integration with various deep
architectures. The code is publicly available at
\href{https://github.com/mindflow-institue/annotation_free_fewshot}{\textcolor{magenta}{GitHub}}.