2021 IEEE International Conference on Image Processing
19-22 September 2021 • Anchorage, Alaska, USA
Imaging Without Borders
| Paper ID | MLR-APPL-IP-2.1 | ||
| Paper Title | GRAPH AFFINITY NETWORK FOR FEW-SHOT SEGMENTATION | ||
| Authors | Xiaoliu Luo, Taiping Zhang, Chongqing University, China | ||
| Session | MLR-APPL-IP-2: Machine learning for image processing 2 | ||
| Location | Area E | ||
| Session Time: | Monday, 20 September, 15:30 - 17:00 | ||
| Presentation Time: | Monday, 20 September, 15:30 - 17:00 | ||
| Presentation | Poster | ||
| Topic | Applications of Machine Learning: Machine learning for image processing | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | Few-shot segmentation aims to learn a segmentation model that can be generalized to novel classes with a few annotations. Previous methods mainly establish the correspondence between support images and query images with global information. However, human perception does not tend to learn a whole representation in its entirety at once. In this paper, we propose a novel network to build the correspondence from subparts, parts and whole. Our network mainly contain two novel designs: we firstly adopt graph convolutional network to make pixels not only contain the information of each pixel itself but also include its contextual pixels, and then a learnable Graph Affinity Module(GAM) is proposed to mine more accurate relationships as well as common object location inference between the support images and the query images. Experiments on the PASCAL-5$^i$ dataset show that our method achieves state-of-the-art performance. | ||