research-article

Mirror Segmentation via Semantic-aware Contextual Contrasted Feature Learning

Authors:

Rynson W. H. LauAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 19, Issue 2s

Article No.: 100, Pages 1 - 22

https://doi.org/10.1145/3566127

Published: 17 February 2023 Publication History

Abstract

Mirrors are everywhere in our daily lives. Existing computer vision systems do not consider mirrors, and hence may get confused by the reflected content inside a mirror, resulting in a severe performance degradation. However, separating the real content outside a mirror from the reflected content inside it is non-trivial. The key challenge is that mirrors typically reflect contents similar to their surroundings, making it very difficult to differentiate the two. In this article, we present a novel method to segment mirrors from a single RGB image. To the best of our knowledge, this is the first work to address the mirror segmentation problem with a computational approach. We make the following contributions: First, we propose a novel network, called MirrorNet+, for mirror segmentation, by modeling both contextual contrasts and semantic associations. Second, we construct the first large-scale mirror segmentation dataset, which consists of 4,018 pairs of images containing mirrors and their corresponding manually annotated mirror masks, covering a variety of daily-life scenes. Third, we conduct extensive experiments to evaluate the proposed method and show that it outperforms the related state-of-the-art detection and segmentation methods. Fourth, we further validate the effectiveness and generalization capability of the proposed semantic awareness contextual contrasted feature learning by applying MirrorNet+ to other vision tasks, i.e., salient object detection and shadow detection. Finally, we provide some applications of mirror segmentation and analyze possible future research directions. Project homepage: https://mhaiyang.github.io/TOMM2022-MirrorNet+/index.html.

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Cited By

MEI HYANG XZHOU YJI GWEI XFAN D(2024)Distraction-aware camouflaged object segmentationSCIENTIA SINICA Informationis10.1360/SSI-2022-013854:3(653)Online publication date: 14-Mar-2024
https://doi.org/10.1360/SSI-2022-0138
Li JXie QChang XXu JLiu Y(2024)Mutually-Guided Hierarchical Multi-Modal Feature Learning for Referring Image SegmentationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/369877120:12(1-18)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3698771
Li YZhou WMeng JYan W(2024)Lightweight and Efficient Multimodal Prompt Injection Network for Scene Parsing of Remote Sensing Scene ImagesIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2024.350778462(1-9)Online publication date: 2024
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Index Terms

Mirror Segmentation via Semantic-aware Contextual Contrasted Feature Learning
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation

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

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 19, Issue 2s

April 2023

545 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3572861

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

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Publication History

Published: 17 February 2023

Online AM: 05 November 2022

Accepted: 25 September 2022

Revised: 27 August 2022

Received: 22 July 2022

Published in TOMM Volume 19, Issue 2s

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National Key Research and Development Program of China
National Natural Science Foundation of China
Innovation Technology Funding of Dalian
Research Grants Council of Hong Kong
Strategic Research Grant from City University of Hong Kong

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Cited By

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https://doi.org/10.1360/SSI-2022-0138
Li JXie QChang XXu JLiu Y(2024)Mutually-Guided Hierarchical Multi-Modal Feature Learning for Referring Image SegmentationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/369877120:12(1-18)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3698771
Li YZhou WMeng JYan W(2024)Lightweight and Efficient Multimodal Prompt Injection Network for Scene Parsing of Remote Sensing Scene ImagesIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2024.350778462(1-9)Online publication date: 2024
https://doi.org/10.1109/TGRS.2024.3507784
Li CZhou WZhou XYan W(2024)Semantic Progressive Guidance Network for RGB-D Mirror SegmentationIEEE Signal Processing Letters10.1109/LSP.2024.347535731(2780-2784)Online publication date: 2024
https://doi.org/10.1109/LSP.2024.3475357
Zhou WCai YDong XQiang FQiu W(2024)ADRNet-S*: Asymmetric depth registration network via contrastive knowledge distillation for RGB-D mirror segmentationInformation Fusion10.1016/j.inffus.2024.102392108(102392)Online publication date: Aug-2024
https://doi.org/10.1016/j.inffus.2024.102392
Wang ZLiu YCheng XIkenaga T(2024)Key points trajectory and multi-level depth distinction based refinement for video mirror and glass segmentationMultimedia Tools and Applications10.1007/s11042-024-19627-583:39(86513-86535)Online publication date: 20-Jun-2024
https://doi.org/10.1007/s11042-024-19627-5
Zhou WCai YZhang LYan WYu L(2023)UTLNet: Uncertainty-Aware Transformer Localization Network for RGB-Depth Mirror SegmentationIEEE Transactions on Multimedia10.1109/TMM.2023.332389026(4564-4574)Online publication date: 11-Oct-2023
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Mei HXu KZhou YWang YPiao HWei XYang X(2023)Camouflaged Object Segmentation with Omni PerceptionInternational Journal of Computer Vision10.1007/s11263-023-01838-2131:11(3019-3034)Online publication date: 1-Nov-2023
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