Generalized Few-shot Semantic Segmentation (GFSS) extends Few-shot Semantic
Segmentation (FSS) to simultaneously segment unseen classes and seen classes
during evaluation. Previous works leverage additional branch or prototypical
aggregation to eliminate the constrained setting of FSS. However,
representation division and embedding prejudice, which heavily results in poor
performance of GFSS, have not been synthetical considered. We address the
aforementioned problems by jointing the prototypical kernel learning and
open-set foreground perception. Specifically, a group of learnable kernels is
proposed to perform segmentation with each kernel in charge of a stuff class.
Then, we explore to merge the prototypical learning to the update of base-class
kernels, which is consistent with the prototype knowledge aggregation of
few-shot novel classes. In addition, a foreground contextual perception module
cooperating with conditional bias based inference is adopted to perform
class-agnostic as well as open-set foreground detection, thus to mitigate the
embedding prejudice and prevent novel targets from being misclassified as
background. Moreover, we also adjust our method to the Class Incremental
Few-shot Semantic Segmentation (CIFSS) which takes the knowledge of novel
classes in a incremental stream. Extensive experiments on PASCAL-5i and
COCO-20i datasets demonstrate that our method performs better than previous
state-of-the-art.

通过联合原型核心学习和开放式前景感知，我们提出了泛化少样本语义分割（GFSS）方法，以准确分割未见过的类别和已见过的类别，在 PASCAL-5i 和 COCO-20i 数据集上表现优于先前的最先进技术。