Weakly supervised instance segmentation is a new research topic in the field of computer vision. Compared with fully supervised instance segmentation, weakly supervised methods use weaker data annotations such as points, scribbles or class labels which are easy to obtain. Among these annotations, image-level instance segmentation using only class labels as supervision is the most challenging task. In this paper, we propose a novel weakly supervised instance segmentation framework using a multi-stage erasing refinement method and a saliency-guided proposals ordering method. Firstly, the multi-stage erasing refinement method is exploited to enhance the instance representation by iteratively discovering separate object-related regions, so as to obtain more complete discriminative regions. Then, the saliency-guided proposals ordering method utilizes the saliency map to alleviate the background noise and better select the object proposals for generating the instance segmentation result. Experimental results on the PASCAL VOC 2012 dataset and the COCO dataset demonstrate that our framework achieves superior performance compared with the state-of-the-art weakly supervised instance segmentation models and the ablation study shows the effectiveness of the proposed two methods.

弱监督实例分割是计算机视觉领域的一个新的研究主题。与完全监督的实例分割相比，弱监督的方法使用了较弱的数据批注，例如易于获得的点，涂鸦或类标签。在这些注释中，仅使用类标签作为监督的图像级实例分割是最具挑战性的任务。在本文中，我们提出了一种使用多阶段擦除细化方法和显着性建议排序方法的新型弱监督实例分割框架。首先，利用多阶段擦除细化方法通过迭代发现与对象相关的独立区域来增强实例表示，从而获得更完整的区分区域。然后，显着性建议书排序方法利用显着性图减轻背景噪声，更好地选择对象建议书以生成实例分割结果。在PASCAL VOC 2012数据集和COCO数据集上的实验结果表明，与最新的弱监督实例分割模型相比，我们的框架具有更高的性能，而消融研究表明了所提出的两种方法的有效性。