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Online Adaptation of Convolutional Neural Networks for Video Object Segmentation
Open accessAbstract
We tackle the task of semi-supervised video object segmentation, i.e. segmenting the pixels belonging to an object in the video using the ground truth pixel mask for the first frame. We build on the recently introduced one-shot video object segmentation (OSVOS) approach which uses a pretrained network and fine-tunes it on the first frame. While achieving impressive performance, at test time OSVOS uses the fine-tuned network in unchanged form and is not able to adapt to large changes in object appearance. To overcome this limitation, we propose Online Adaptive Video Object Segmentation (OnAVOS) which updates the network online using training examples selected based on the confidence of the network and the spatial configuration. Additionally, we add a pretraining step based on objectness, which is learned on PASCAL. Our experiments show that both extensions are highly effective and improve the state of the art on DAVIS to an intersection-over-union score of 85.7%.
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“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 [56]
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Video Object Segmentation by Learning Location-Sensitive Embeddings
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Continuous Adaptation for Interactive Object Segmentation by Learning from Corrections
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Metadata
- Authors
- Voigtlaender, P., & Leibe, B.
- Affiliations
- RWTH Aachen University (DE)
- ORCID
- Leibe, Bastian
- Venue
- Procedings of the British Machine Vision Conference 2017
- Publisher
- British Machine Vision Association
- Published
- 2017
- OpenAlex
- W2724418412
- Cited by
- 443
- References
- 36
- Citations by year
- 2026: 4 · 2025: 24 · 2024: 40 · 2023: 53 · 2022: 76 · 2021: 75 · 2020: 74 · 2019: 66 · 2018: 30 · 2017: 1
- FWCI
- 13.67
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- British Machine Vision Conference (BMVC) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- RWTH Publications (RWTH Aachen) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- video object segmentation, semi-supervised learning, online adaptation, fine-tuning, PASCAL dataset, spatial configuration
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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