research-article

Complementary Coarse-to-Fine Matching for Video Object Segmentation

Authors:

Shiliang ZhangAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 19, Issue 6

Article No.: 203, Pages 1 - 21

https://doi.org/10.1145/3596496

Published: 12 July 2023 Publication History

Abstract

Semi-supervised Video Object Segmentation (VOS) needs to establish pixel-level correspondences between a video frame and preceding segmented frames to leverage their segmentation clues. Most works rely on features at a single scale to establish those correspondences, e.g., perform dense matching with Convolutional Neural Network (CNN) features from a deep layer. Differently, this work explores complementary features at different scales to pursue more robust feature matching. A coarse feature from a deep layer is first adopted to get coarse pixel-level correspondences. We hence evaluate the quality of those correspondences, and select pixels with low-quality correspondences for fine-scale feature matching. Segmentation clues of previous frames are propagated by both coarse and fine-scale correspondences, which are fused with appearance features for object segmentation. Compared with previous works, this coarse-to-fine matching scheme is more robust to distractions by similar objects and better preserves object details. The sparse fine-scale matching also ensures a fast inference speed. On popular VOS datasets including DAVIS and YouTube-VOS, the proposed method shows promising performance compared with recent works.

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Index Terms

Complementary Coarse-to-Fine Matching for Video Object Segmentation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Tracking
        Video segmentation

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Information & Contributors

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 19, Issue 6

November 2023

858 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3599695

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

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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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Association for Computing Machinery

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Publication History

Published: 12 July 2023

Online AM: 16 May 2023

Accepted: 29 April 2023

Revised: 15 March 2023

Received: 31 July 2022

Published in TOMM Volume 19, Issue 6

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Cited By

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https://doi.org/10.1145/3678176
Jiang XYao YLiu SShen FNie LHua X(2024)Dual Dynamic Threshold Adjustment StrategyACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365604720:7(1-18)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3656047
Liu WCai JLi QLiao CCao JHe SYu Y(2024)Learning Nighttime Semantic Segmentation the Hard WayACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365003220:7(1-23)Online publication date: 16-May-2024
https://dl.acm.org/doi/10.1145/3650032
Feng ZXu JMa LZhang S(2024)Efficient Video Transformers via Spatial-temporal Token Merging for Action RecognitionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363378120:4(1-21)Online publication date: 11-Jan-2024
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Fu FFang SChen WMao Z(2024)Sentiment-Oriented Transformer-Based Variational Autoencoder Network for Live Video CommentingACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363333420:4(1-24)Online publication date: 11-Jan-2024
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Chen SYang LHu Y(2023)Video Captioning Based on Cascaded Attention-Guided Visual Feature FusionNeural Processing Letters10.1007/s11063-023-11386-y55:8(11509-11526)Online publication date: 25-Aug-2023
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