  Few-shot semantic segmentation models aim to segment images after learning
from only a few annotated examples. A key challenge for them is how to avoid
overfitting because limited training data is available. While prior works
usually limited the overall model capacity to alleviate overfitting, this
hampers segmentation accuracy. We demonstrate how to increase overall model
capacity to achieve improved performance, by introducing objectness, which is
class-agnostic and so not prone to overfitting, for complementary use with
class-specific features. Extensive experiments demonstrate the versatility of
our simple approach of introducing objectness for different base architectures
that rely on different data loaders and training schedules (DENet, PFENet) as
well as with different backbone models (ResNet-50, ResNet-101 and HRNetV2-W48).
Given only one annotated example of an unseen category, experiments show that
our method outperforms state-of-art methods with respect to mIoU by at least
4.7% and 1.5% on PASCAL-5i and COCO-20i respectively.
