The DOI matches its Crossref record; 38 of the 40 references it cites were found in a registry or an index.
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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Citations
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.322 · Cited by 29,137
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Finn, C., Abbeel, P., & Levine, S. (2017) · arXiv · doi:10.48550/arxiv.1703.03400 · Cited by 8,512
A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation
Perazzi, F., Pont-Tuset, J., McWilliams, B., Gool, L. V., Groß, M. H., & Sorkine‐Hornung, A. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.85 · Cited by 2,039
Fast Online Object Tracking and Segmentation: A Unifying Approach
Wang, Q., Zhang, L., Bertinetto, L., Hu, W., & Torr, P. H. S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00142 · Cited by 1,554
Learning Discriminative Model Prediction for Tracking
Bhat, G., Danelljan, M., Van Gool, L., & Timofte, R. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00628 · Cited by 1,515
Self-citationMeta-Learning With Differentiable Convex Optimization
Lee, K., Maji, S., Ravichandran, A., & Soatto, S. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.01091 · Cited by 1,351
Berman, M., Triki, A. R., & Blaschko, M. B. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00464 · Cited by 1,163
Research works printed but not linked: 2
- Behl, H.S., Najafi, M., Arnab, A., Torr, P.H.S.: Meta learning deep visual words for fast video object segmentation. In: NeurIPS 2019 Workshop on Machine Learning for Autonomous Driving (2018) · DOI
- Yang, L., Wang, Y., Xiong, X., Yang, J., Katsaggelos, A.K.: Efficient video object segmentation via network modulation. Algorithms 29, 15 (2018)
Cited by
Sorted by citations
XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model
Cheng, H. K., & Schwing, A. G. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-19815-1_37 · Cited by 449
A Survey on Deep Learning Technique for Video Segmentation
Zhou, T., Porikli, F., Crandall, D., Gool, L. V., & Wang, W. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3225573 · Cited by 291
The Eighth Visual Object Tracking VOT2020 Challenge Results
Kristan, M., Leonardis, A., Matas, J., Felsberg, M., Pflugfelder, R. P., Kämäräinen, J., Danelljan, M., Zajc, L. Č., Lukežič, A., Drbohlav, O., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-68238-5_39 · Cited by 262
Self-citationRethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object SegmentationPreprint
Cheng, H. K., Tai, Y., & Tang, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.05210 · Cited by 224
Spiking Transformers for Event-based Single Object Tracking
Zhang, J., Dong, B., Zhang, H., Ding, J., Heide, F., Yin, B., & Yang, X. S. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00860 · Cited by 212
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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