Task-Disruptive Background Suppression for Few-Shot Segmentation
Suho Park SuBeen Lee Sangeek Hyun Hyun Seok Seong Jae-Pil Heo
Few-shot segmentation aims to accurately segment novel target objects within
query images using only a limited number of annotated support images. The
recent works exploit support background as well as its foreground to precisely
compute the dense correlations between query and support. However, they
overlook the characteristics of the background that generally contains various
types of objects. In this paper, we highlight this characteristic of background
which can bring problematic cases as follows: (1) when the query and support
backgrounds are dissimilar and (2) when objects in the support background are
similar to the target object in the query. Without any consideration of the
above cases, adopting the entire support background leads to a misprediction of
the query foreground as background. To address this issue, we propose
Task-disruptive Background Suppression (TBS), a module to suppress those
disruptive support background features based on two spatial-wise scores:
query-relevant and target-relevant scores. The former aims to mitigate the
impact of unshared features solely existing in the support background, while
the latter aims to reduce the influence of target-similar support background
features. Based on these two scores, we define a query background relevant
score that captures the similarity between the backgrounds of the query and the
support, and utilize it to scale support background features to adaptively
restrict the impact of disruptive support backgrounds. Our proposed method
achieves state-of-the-art performance on PASCAL-5 and COCO-20 datasets on
1-shot segmentation. Our official code is available at
github.com/SuhoPark0706/TBSNet.