Weakly-supervised instance segmentation, which could greatly save labor and time cost of pixel mask annotation, has attracted increasing attention in recent years. The commonly used pipeline firstly utilizes conventional image segmentation methods to automatically generate initial masks and then use them to train an off-the-shelf segmentation network in an iterative way. However, the initial generated masks usually contains a notable proportion of invalid masks which are mainly caused by small object instances. Directly using these initial masks to train segmentation models is harmful for the performance. To address this problem, we propose a kind of hybrid networks in this paper. In our architecture, there is a principle segmentation network which is used to handle the normal samples with valid generated masks. In addition, a complementary branch is added to handle the small and dim objects without valid masks. Experimental results indicate that our method can achieve significantly performance improvement both on the small object instances and large ones, and outperforms all state-of-the-art methods.
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 twostage 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.
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