Weakly Supervised Instance Segmentation Using Hybrid Network
This work addresses the challenge of reducing annotation costs in instance segmentation for computer vision applications, but it is incremental as it builds on existing pipelines with a novel branch.
The paper tackles the problem of invalid masks in weakly supervised instance segmentation, particularly for small objects, by proposing a hybrid network with a complementary branch for small and dim objects, achieving significant performance improvement and outperforming state-of-the-art methods.
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 model is harmful for the performance. To address this problem, we propose a hybrid network 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.