research-article

Fast Real-Time Video Object Segmentation with a Tangled Memory Network

Authors:

Yong LiuAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology, Volume 14, Issue 3

Article No.: 51, Pages 1 - 21

https://doi.org/10.1145/3585076

Published: 13 April 2023 Publication History

Abstract

In this article, we present a fast real-time tangled memory network that segments the objects effectively and efficiently for semi-supervised video object segmentation (VOS). We propose a tangled reference encoder and a memory bank organization mechanism based on a state estimator to fully utilize the mask features and alleviate memory overhead and computational burden brought by the unlimited memory bank used in many memory-based methods. First, the tangled memory network exploits the mask features that uncover abundant object information like edges and contours but are not fully explored in existing methods. Specifically, a tangled two-stream reference encoder is designed to extract and fuse the features from both RGB frames and the predicted masks. Second, to indicate the quality of the predicted mask and feedback the online prediction state for organizing the memory bank, we devise a target state estimator to learn the IoU score between the predicted mask and ground truth. Moreover, to accelerate the forward process and avoid memory overflow, we use a memory bank of fixed size to store historical features by designing a new efficient memory bank organization mechanism based on the mask state score provided by the state estimator. We conduct comprehensive experiments on the public benchmarks DAVIS and YouTube-VOS, demonstrating that our method obtains competitive results while running at high speed (66 FPS on the DAVIS16-val set).

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Cited By

He ZLi D(2024)Real-time segmentation of short videos under VR technology in dynamic scenesJournal of Intelligent Systems10.1515/jisys-2023-028933:1Online publication date: 24-May-2024
https://doi.org/10.1515/jisys-2023-0289
Ra JWang MMei JLiu SYang YLiu Y(2024)Exploit Spatiotemporal Contextual Information for 3D Single Object Tracking via Memory Networks2024 International Conference on 3D Vision (3DV)10.1109/3DV62453.2024.00050(842-851)Online publication date: 18-Mar-2024
https://doi.org/10.1109/3DV62453.2024.00050
Mei JYang YWang MLi ZRa JLiu Y(2024)LiDAR video object segmentation with dynamic kernel refinementPattern Recognition Letters10.1016/j.patrec.2023.12.013178(21-27)Online publication date: Feb-2024
https://doi.org/10.1016/j.patrec.2023.12.013
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Index Terms

Fast Real-Time Video Object Segmentation with a Tangled Memory Network
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Tracking
        Video segmentation
      2. Computer vision tasks
        Scene understanding

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 14, Issue 3

June 2023

451 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3587032

Editor:
Huan Liu
Arizona State University, USA

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Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 13 April 2023

Online AM: 23 February 2023

Accepted: 13 February 2023

Revised: 10 February 2023

Received: 02 August 2022

Published in TIST Volume 14, Issue 3

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Cited By

He ZLi D(2024)Real-time segmentation of short videos under VR technology in dynamic scenesJournal of Intelligent Systems10.1515/jisys-2023-028933:1Online publication date: 24-May-2024
https://doi.org/10.1515/jisys-2023-0289
Ra JWang MMei JLiu SYang YLiu Y(2024)Exploit Spatiotemporal Contextual Information for 3D Single Object Tracking via Memory Networks2024 International Conference on 3D Vision (3DV)10.1109/3DV62453.2024.00050(842-851)Online publication date: 18-Mar-2024
https://doi.org/10.1109/3DV62453.2024.00050
Mei JYang YWang MLi ZRa JLiu Y(2024)LiDAR video object segmentation with dynamic kernel refinementPattern Recognition Letters10.1016/j.patrec.2023.12.013178(21-27)Online publication date: Feb-2024
https://doi.org/10.1016/j.patrec.2023.12.013
Nguyen HKim CLi F(2024)Space–time recurrent memory networkComputer Vision and Image Understanding10.1016/j.cviu.2024.103943241:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.cviu.2024.103943
Mei JWang MYang YLi ZLiu Y(2024)Learning spatiotemporal relationships with a unified framework for video object segmentationApplied Intelligence10.1007/s10489-024-05486-y54:8(6138-6153)Online publication date: 7-May-2024
https://dl.acm.org/doi/10.1007/s10489-024-05486-y

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