1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.
Image semantic segmentation tasks and methods based on weakly supervised conditions have been proposed and achieve better and better performance in recent years. However, the purpose of these tasks is mainly to simplify the labeling work. In this paper, we establish a new and more challenging task condition: weaklier supervision with one image level annotation per category, which only provides prior knowledge that humans need to recognize new objects, and aims to achieve pixel-level object semantic understanding. In order to solve this problem, a three-stage semantic segmentation framework is put forward, which realizes image level, pixel level, and object common features learning from coarse to fine grade, and finally obtains semantic segmentation results with accurate and complete object regions. Researches on PASCAL VOC 2012 dataset demonstrates the effectiveness of the proposed method, which makes an obvious improvement compared to baselines. Based on fewer supervised information, the method also provides satisfactory performance compared to weakly supervised learning-based methods with complete image-level annotations.
© Copyright 2020 IEEE – All rights reserved. Use of this website signifies your agreement to the IEEE Terms and Conditions.
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.