Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps

Yuk Heo; Yeong Jun Koh; Chang Su Kim

doi:10.1109/CVPR46437.2021.00724

Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps

Yuk Heo
, Yeong Jun Koh
, Chang Su Kim

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

26 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Proceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021
Publisher	IEEE Computer Society
Pages	7318-7326
Number of pages	9
ISBN (Electronic)	9781665445092
DOIs	https://doi.org/10.1109/CVPR46437.2021.00724
Publication status	Published - 2021
Event	2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021 - Virtual, Online, United States Duration: 2021 Jun 19 → 2021 Jun 25

Publication series

Name	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)	1063-6919

Conference

Conference	2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021
Country/Territory	United States
City	Virtual, Online
Period	21/6/19 → 21/6/25

Bibliographical note

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

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition

Access to Document

10.1109/CVPR46437.2021.00724

Cite this

Heo, Y., Koh, Y. J., & Kim, C. S. (2021). Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps. In Proceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021 (pp. 7318-7326). (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). IEEE Computer Society. https://doi.org/10.1109/CVPR46437.2021.00724

Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps. / Heo, Yuk; Koh, Yeong Jun; Kim, Chang Su.
Proceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021. IEEE Computer Society, 2021. p. 7318-7326 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Heo, Y, Koh, YJ & Kim, CS 2021, Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps. in Proceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, pp. 7318-7326, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021, Virtual, Online, United States, 21/6/19. https://doi.org/10.1109/CVPR46437.2021.00724

Heo Y, Koh YJ, Kim CS. Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps. In Proceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021. IEEE Computer Society. 2021. p. 7318-7326. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). doi: 10.1109/CVPR46437.2021.00724

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title = "Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps",

abstract = "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.",

author = "Yuk Heo and Koh, \{Yeong Jun\} and Kim, \{Chang Su\}",

note = "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: {\textcopyright} 2021 IEEE; 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021 ; Conference date: 19-06-2021 Through 25-06-2021",

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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.

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Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps

Abstract

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