Few-shot semantic segmentation models aim to segment images after learning from only a few annotated examples. A key challenge for them is overfitting. Prior works usually limit the overall model capacity to alleviate overfitting, but the limited capacity also hampers the segmentation accuracy. We instead propose a method that increases the overall model capacity by supplementing class-specific features with objectness, which is class-agnostic and so not prone to overfitting. Extensive experiments demonstrate the versatility of our method with multiple backbone models (ResNet-50, ResNet-101 and HRNetV2-W48) and existing base architectures (DENet and PFENet). 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.
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