In this paper, we consider salient instance segmentation. As well as producing bounding boxes,our network also outputs high-quality instance-level segments as initial selections to indicate the regions of interest. Ta...In this paper, we consider salient instance segmentation. As well as producing bounding boxes,our network also outputs high-quality instance-level segments as initial selections to indicate the regions of interest. Taking into account the category-independent property of each target, we design a single stage salient instance segmentation framework, with a novel segmentation branch. Our new branch regards not only local context inside each detection window but also the surrounding context, enabling us to distinguish instances in the same scope even with partial occlusion.Our network is end-to-end trainable and is fast(running at 40 fps for images with resolution 320 × 320). We evaluate our approach on a publicly available benchmark and show that it outperforms alternative solutions. We also provide a thorough analysis of our design choices to help readers better understand the function of each part of our network. Source code can be found at https://github.com/Ruochen Fan/S4 Net.展开更多
基金supported by National Natural Science Foundation of China(61521002,61572264,61620106008)the National Youth Talent Support Program+1 种基金Tianjin Natural Science Foundation(17JCJQJC43700,18ZXZNGX00110)the Fundamental Research Funds for the Central Universities(Nankai University,No.63191501)。
文摘In this paper, we consider salient instance segmentation. As well as producing bounding boxes,our network also outputs high-quality instance-level segments as initial selections to indicate the regions of interest. Taking into account the category-independent property of each target, we design a single stage salient instance segmentation framework, with a novel segmentation branch. Our new branch regards not only local context inside each detection window but also the surrounding context, enabling us to distinguish instances in the same scope even with partial occlusion.Our network is end-to-end trainable and is fast(running at 40 fps for images with resolution 320 × 320). We evaluate our approach on a publicly available benchmark and show that it outperforms alternative solutions. We also provide a thorough analysis of our design choices to help readers better understand the function of each part of our network. Source code can be found at https://github.com/Ruochen Fan/S4 Net.
