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Cited by 927Advanced Neural Network ApplicationsVideo Surveillance and Tracking MethodsDomain Adaptation and Few-Shot Learning

One-Shot Video Object Segmentation

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Caelles, S., Maninis, K.-K., Pont-Tuset, J., Leal-Taixe, L., Cremers, D., Van Gool, L.·2017·2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
DOI 10.1109/cvpr.2017.565
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Abstract

This paper tackles the task of semi-supervised video object segmentation, i.e., the separation of an object from the background in a video, given the mask of the first frame. We present One-Shot Video Object Segmentation (OSVOS), based on a fully-convolutional neural network architecture that is able to successively transfer generic semantic information, learned on ImageNet, to the task of foreground segmentation, and finally to learning the appearance of a single annotated object of the test sequence (hence one-shot). Although all frames are processed independently, the results are temporally coherent and stable. We perform experiments on two annotated video segmentation databases, which show that OSVOS is fast and improves the state of the art by a significant margin (79.8% vs 68.0%).

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  • Adopting a memory architecture to store object information from past frames is indispensable for accurately tracking objects in video.

    “To address the above challenges, adopting a memory architecture to store the object information from past frames is indispensable for accurately tracking objects in video [5,7,10,29,40,52,56,57,64,66,68].”

    From SAM2LONG: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory Tree (2025) · cites this paper as [7]

    The Conclusions (p. 8) say OSVOS achieves highly accurate, temporally consistent segmentation while processing each frame independently, without explicitly modeling temporal consistency. The discussion on p. 2 likewise says its object model produces stable results even when frames are processed independently. This contradicts the claim that storing information from past frames is indispensable. (LLM, conf=0.94)

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Research works printed but not linked: 47

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Cited by

Sorted by citations

Authors
Caelles, S., Maninis, K.-K., Pont-Tuset, J., Leal-Taixe, L., Cremers, D., & Van Gool, L.
Affiliations
  • ETH Zurich (CH)
  • Technical University of Munich (DE)
ORCID
Venue
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
IEEE
Published
2017-07
Pages
5320-5329
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.2017.565
OpenAlex
W2552531400
Cited by
927
References
7
Citations by year
2026: 19 · 2025: 51 · 2024: 88 · 2023: 112 · 2022: 139 · 2021: 133 · 2020: 139 · 2019: 155 · 2018: 72 · 2017: 17 · 2016: 2
FWCI
30.61
Type
conference paper
Language
en
Open access
Licence
other-oa
Locations
  • IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
  • Repository for Publications and Research Data (ETH Zurich) (acceptedVersion, other-oa)
  • arXiv (Cornell University) (submittedVersion)
  • Repository for Publications and Research Data (ETH Zurich) (submittedVersion, other-oa)
  • arXiv (Cornell University)
  • ETH Zürich Research Collection
Funders
  • Nvidia
  • Kommission für Technologie und Innovation
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords
video object segmentation, one-shot learning, semi-supervised learning, foreground segmentation, fully convolutional network, temporal coherence

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