Name
Abstract | ||
|---|---|---|
Building a large image dataset with high-quality object masks for semantic segmentation is costly and time-consuming. In this paper, we introduce a principled semi-supervised framework that only use a small set of fully supervised images (having semantic segmentation labels and box labels) and a set of images with only object bounding box labels (we call it the weak-set). Our framework trains the primary segmentation model with the aid of an ancillary model that generates initial segmentation labels for the weak-set and a self-correction module that improves the generated labels during training using the increasingly accurate primary model. We introduce two variants of the self-correction module using either linear or convolutional functions. Experiments on the PASCAL VOC 2012 and Cityscape datasets show that our models trained with a small fully supervised set perform similar to, or better than, models trained with a large fully supervised set while requiring 7x less annotation effort. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/CVPR42600.2020.01273 | CVPR |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
46 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mostafa S. Ibrahim | 1 | 51 | 2.98 |
| Vahdat, Arash | 2 | 353 | 18.20 |
| Mani Ranjbar | 3 | 153 | 10.40 |
| William G. Macready | 4 | 161 | 39.07 |