TY - GEN
T1 - Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps
AU - Heo, Yuk
AU - Koh, Yeong Jun
AU - Kim, Chang Su
N1 - Funding Information:
This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by MSIT, Korea (No. NRF-2018R1A2B3003896), in part by MSIT, Korea, under the ITRC support program (IITP-2020-2016-0-00464) supervised by the IITP, and in part by the NRF grant funded by MSIT, Korea (No. NRF-2019R1F1A1062907).
Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - We propose a novel guided interactive segmentation (GIS) algorithm for video objects to improve the segmentation accuracy and reduce the interaction time. First, we design the reliability-based attention module to analyze the reliability of multiple annotated frames. Second, we develop the intersection-aware propagation module to propagate segmentation results to neighboring frames. Third, we introduce the GIS mechanism for a user to select unsatisfactory frames quickly with less effort. Experimental results demonstrate that the proposed algorithm provides more accurate segmentation results at a faster speed than conventional algorithms. Codes are available at https://github.com/yuk6heo/GIS-RAmap.
AB - We propose a novel guided interactive segmentation (GIS) algorithm for video objects to improve the segmentation accuracy and reduce the interaction time. First, we design the reliability-based attention module to analyze the reliability of multiple annotated frames. Second, we develop the intersection-aware propagation module to propagate segmentation results to neighboring frames. Third, we introduce the GIS mechanism for a user to select unsatisfactory frames quickly with less effort. Experimental results demonstrate that the proposed algorithm provides more accurate segmentation results at a faster speed than conventional algorithms. Codes are available at https://github.com/yuk6heo/GIS-RAmap.
UR - http://www.scopus.com/inward/record.url?scp=85123046508&partnerID=8YFLogxK
U2 - 10.1109/CVPR46437.2021.00724
DO - 10.1109/CVPR46437.2021.00724
M3 - Conference contribution
AN - SCOPUS:85123046508
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 7318
EP - 7326
BT - Proceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021
PB - IEEE Computer Society
T2 - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021
Y2 - 19 June 2021 through 25 June 2021
ER -