TY - GEN
T1 - Weakly Supervised Segmentation of Small Buildings with Point Labels
AU - Lee, Jae Hun
AU - Kim, Chan Young
AU - Sull, Sanghoon
N1 - 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
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85127784468&partnerID=8YFLogxK
U2 - 10.1109/ICCV48922.2021.00731
DO - 10.1109/ICCV48922.2021.00731
M3 - Conference contribution
AN - SCOPUS:85127784468
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 7386
EP - 7395
BT - Proceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE/CVF International Conference on Computer Vision, ICCV 2021
Y2 - 11 October 2021 through 17 October 2021
ER -