Existing studies in few-shot semantic segmentation only focus on mining the
target object information, however, often are hard to tell ambiguous regions,
especially in non-target regions, which include background (BG) and Distracting
Objects (DOs). To alleviate this problem, we propose a novel framework, namely
Non-Target Region Eliminating (NTRE) network, to explicitly mine and eliminate
BG and DO regions in the query. First, a BG Mining Module (BGMM) is proposed to
extract the BG region via learning a general BG prototype. To this end, we
design a BG loss to supervise the learning of BGMM only using the known target
object segmentation ground truth. Then, a BG Eliminating Module and a DO
Eliminating Module are proposed to successively filter out the BG and DO
information from the query feature, based on which we can obtain a BG and
DO-free target object segmentation result. Furthermore, we propose a
prototypical contrastive learning algorithm to improve the model ability of
distinguishing the target object from DOs. Extensive experiments on both
PASCAL-5i and COCO-20i datasets show that our approach is effective despite its
simplicity.

本研究提出了一种名为 NTRE 网络的新框架，包含 BG Mining&Eliminating Module 和 DO Eliminating Module，可以显式地挖掘和消除查询中的 BG 和 DO 区域以进行 few-shot 语义分割，并提出了基于原型对比学习算法来提高模型区分目标对象与 DOs 的能力。在 PASCAL-5i 和 COCO-20i 数据集上的广泛实验表明，我们的方法虽然简单，但非常有效。