  Semantic segmentation is a key prerequisite to robust image understanding for
applications in \acrlong{ai} and Robotics. \acrlong{fss}, in particular,
concerns the extension and optimization of traditional segmentation methods in
challenging conditions where limited training examples are available. A
predominant approach in \acrlong{fss} is to rely on a single backbone for
visual feature extraction. Choosing which backbone to leverage is a deciding
factor contributing to the overall performance. In this work, we interrogate on
whether fusing features from different backbones can improve the ability of
\acrlong{fss} models to capture richer visual features. To tackle this
question, we propose and compare two ensembling techniques-Independent Voting
and Feature Fusion. Among the available \acrlong{fss} methods, we implement the
proposed ensembling techniques on PANet. The module dedicated to predicting
segmentation masks from the backbone embeddings in PANet avoids trainable
parameters, creating a controlled `in vitro' setting for isolating the impact
of different ensembling strategies. Leveraging the complementary strengths of
different backbones, our approach outperforms the original single-backbone
PANet across standard benchmarks even in challenging one-shot learning
scenarios. Specifically, it achieved a performance improvement of +7.37\% on
PASCAL-5\textsuperscript{i} and of +10.68\% on COCO-20\textsuperscript{i} in
the top-performing scenario where three backbones are combined. These results,
together with the qualitative inspection of the predicted subject masks,
suggest that relying on multiple backbones in PANet leads to a more
comprehensive feature representation, thus expediting the successful
application of \acrlong{fss} methods in challenging, data-scarce environments.
