How to extract instance-level masks without instance-level supervision is the
main challenge of weakly supervised instance segmentation (WSIS). Popular WSIS
methods estimate a displacement field (DF) via learning inter-pixel relations
and perform clustering to identify instances. However, the resulting instance
centroids are inherently unstable and vary significantly across different
clustering algorithms. In this paper, we propose Boundary-Assisted Instance
Segmentation (BAISeg), which is a novel paradigm for WSIS that realizes
instance segmentation with pixel-level annotations. BAISeg comprises an
instance-aware boundary detection (IABD) branch and a semantic segmentation
branch. The IABD branch identifies instances by predicting class-agnostic
instance boundaries rather than instance centroids, therefore, it is different
from previous DF-based approaches. In particular, we proposed the Cascade
Fusion Module (CFM) and the Deep Mutual Attention (DMA) in the IABD branch to
obtain rich contextual information and capture instance boundaries with weak
responses. During the training phase, we employed Pixel-to-Pixel Contrast to
enhance the discriminative capacity of the IABD branch. This further
strengthens the continuity and closedness of the instance boundaries. Extensive
experiments on PASCAL VOC 2012 and MS COCO demonstrate the effectiveness of our
approach, and we achieve considerable performance with only pixel-level
annotations. The code will be available at this https URL

我们提出了一种新的弱监督实例分割方法，通过预测类不可知的实例边界而不是实例中心点来识别实例，进一步增强了实例边界的连续性和闭合性，并且在只使用像素级注释的情况下取得了可观的性能。