  Few-shot segmentation (FSS) aims to segment unseen classes using a few
annotated samples. Typically, a prototype representing the foreground class is
extracted from annotated support image(s) and is matched to features
representing each pixel in the query image. However, models learnt in this way
are insufficiently discriminatory, and often produce false positives:
misclassifying background pixels as foreground. Some FSS methods try to address
this issue by using the background in the support image(s) to help identify the
background in the query image. However, the backgrounds of theses images is
often quite distinct, and hence, the support image background information is
uninformative. This article proposes a method, QSR, that extracts the
background from the query image itself, and as a result is better able to
discriminate between foreground and background features in the query image.
This is achieved by modifying the training process to associate prototypes with
class labels including known classes from the training data and latent classes
representing unknown background objects. This class information is then used to
extract a background prototype from the query image. To successfully associate
prototypes with class labels and extract a background prototype that is capable
of predicting a mask for the background regions of the image, the machinery for
extracting and using foreground prototypes is induced to become more
discriminative between different classes. Experiments for both 1-shot and
5-shot FSS on both the PASCAL-5i and COCO-20i datasets demonstrate that the
proposed method results in a significant improvement in performance for the
baseline methods it is applied to. As QSR operates only during training, these
improved results are produced with no extra computational complexity during
testing.
