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Learning What to Learn for Video Object Segmentation
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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 [5]
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Cited by
Sorted by citations
XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model
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Metadata
- Authors
- Bhat, G., Lawin, F. J., Danelljan, M., Robinson, A., Felsberg, M., Van Gool, L., & Timofte, R.
- Affiliations
- ETH Zurich (CH)
- Linköping University (SE)
- Corresponding author
- Bhat, Goutam
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 777-794
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3013056670
- Cited by
- 160
- References
- 40
- Citations by year
- 2026: 4 · 2025: 22 · 2024: 27 · 2023: 38 · 2022: 33 · 2021: 33 · 2020: 3
- FWCI
- 8.25
- Type
- conference paper
- Language
- en
- Open access
- green
- lirias.kuleuven.be
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Lirias (acceptedVersion, other-oa)
- arXiv (Cornell University) (submittedVersion)
- Diva portal (Dalarna University Library) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- Nvidia
- Eidgenössische Technische Hochschule Zürich
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- 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
- Keywords
- video object segmentation, few-shot learning
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