Name
Title | ||
|---|---|---|
Unsupervised Meta-learning of Figure-Ground Segmentation via Imitating Visual Effects. |
Abstract | ||
|---|---|---|
This paper presents a "learning to learn" approach to figure-ground image segmentation. By exploring webly-abundant images of specific visual effects, our method can effectively learn the visual-effect internal representations in an unsupervised manner and uses this knowledge to differentiate the figure from the ground in an image. Specifically, we formulate the meta-learning process as a compositional image editing task that learns to imitate a certain visual effect and derive the corresponding internal representation. Such a generative process can help instantiate the underlying figure-ground notion and enables the system to accomplish the intended image segmentation. Whereas existing generative methods are mostly tailored to image synthesis or style transfer, our approach offers a flexible learning mechanism to model a general concept of figure-ground segmentation from unorganized images that have no explicit pixel-level annotations. We validate our approach via extensive experiments on six datasets to demonstrate that the proposed model can be end-to-end trained without ground-truth pixel labeling yet outperforms the existing methods of unsupervised segmentation tasks. |
Year | Venue | Field |
|---|---|---|
2018 | THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | Computer vision,Computer science,Segmentation,Figure–ground,Artificial intelligence,Machine learning |
DocType | Volume | Citations |
Journal | abs/1812.08442 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ding-Jie Chen | 1 | 31 | 6.70 |
| Jui-Ting Chien | 2 | 34 | 3.09 |
| Hwann-Tzong Chen | 3 | 826 | 52.13 |
| Tyng-Luh Liu | 4 | 1384 | 85.56 |