Abstract
Most supervised image segmentation methods require delicate and time-consuming pixel-level labeling of building or objects, especially for small objects. In this paper, we present a weakly supervised segmentation network for aerial/satellite images, separately considering small and large objects. First, we propose a simple point labeling method for small objects, while large objects are fully labeled. Then, we present a segmentation network trained with a small object mask to separate small and large objects in the loss function. During training, we employ a memory bank to cope with the limited number of point labels. Experiments results with three public datasets demonstrate the feasibility of our approach.
Original language | English |
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Title of host publication | Proceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 7386-7395 |
Number of pages | 10 |
ISBN (Electronic) | 9781665428125 |
DOIs | |
Publication status | Published - 2021 |
Event | 18th IEEE/CVF International Conference on Computer Vision, ICCV 2021 - Virtual, Online, Canada Duration: 2021 Oct 11 → 2021 Oct 17 |
Publication series
Name | Proceedings of the IEEE International Conference on Computer Vision |
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ISSN (Print) | 1550-5499 |
Conference
Conference | 18th IEEE/CVF International Conference on Computer Vision, ICCV 2021 |
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Country/Territory | Canada |
City | Virtual, Online |
Period | 21/10/11 → 21/10/17 |
Bibliographical note
Funding Information:Acknowledgements. This work was conducted by Center for Applied Research in Artificial Intelligence (CARAI) grant funded by Defense Acquisition Program Administration (DAPA) and Agency for Defense Development (ADD) (UD190031RD).
Publisher Copyright:
© 2021 IEEE
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition