Learning What to Learn for Video Object Segmentation
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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 [5]
Section 3.5 (p. 10) describes retaining samples from past frames to update the target model, and the ablative study (p. 26) reports that updating it improves segmentation performance. The paper does not establish that a memory architecture is indispensable for accurate tracking; it reports a performance benefit over using the first-frame model without updates. (LLM, conf=0.87)
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Is First Person Vision Challenging for Object Tracking?
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Learning Maritime Obstacle Detection from Weak Annotations by Scaffolding
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Prototypical Matching Networks for Video Object Segmentation
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Segment Every Reference Object in Spatial and Temporal Spaces
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Drone-Person Tracking in Uniform Appearance Crowd: A New Dataset
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Beyond traditional visual object tracking: a survey
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Remote Health Prediction System Using Machine Learning Algorithms
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- 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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