Weakly Supervised Instance Segmentation Based on Two-Stage Transfer Learning

Weakly supervised instance segmentation, which could greatly decrease financial and time cost, is one of fundamental computer vision tasks. State-of-the-art methods mainly concentrate on improving the quality of generated pixel level labels, namely masks, using complex traditional segmentation methods, and ignore the effect of the quality of generated masks. Namely, the masks of small object instances tend to be invalid, which would degrades the performance of instance segmentation. In this paper, we propose a two-stage transfer learning framework for weakly supervised instance segmentation. We explicitly discriminate the invalid and valid generated masks, and just utilize the valid masks for training to avoid the interference of invalid ones. We use a network-based transfer learning strategy to effectively utilize all useful information, including category labels and bounding-box information of all objects and valid generated masks. Besides, we further use a feature-mapping-based transfer learning strategy to improve the performance of small object instance segmentation. We demonstrate the effectiveness of the proposed method on the PASCAL VOC 2012, and the experimental results show that our proposed method is effective and outperforms state-of-the-art methods.