  We study few-shot semantic segmentation that aims to segment a target object
from a query image when provided with a few annotated support images of the
target class. Several recent methods resort to a feature masking (FM) technique
to discard irrelevant feature activations which eventually facilitates the
reliable prediction of segmentation mask. A fundamental limitation of FM is the
inability to preserve the fine-grained spatial details that affect the accuracy
of segmentation mask, especially for small target objects. In this paper, we
develop a simple, effective, and efficient approach to enhance feature masking
(FM). We dub the enhanced FM as hybrid masking (HM). Specifically, we
compensate for the loss of fine-grained spatial details in FM technique by
investigating and leveraging a complementary basic input masking method.
Experiments have been conducted on three publicly available benchmarks with
strong few-shot segmentation (FSS) baselines. We empirically show improved
performance against the current state-of-the-art methods by visible margins
across different benchmarks. Our code and trained models are available at:
https://github.com/moonsh/HM-Hybrid-Masking
