The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
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%.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
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
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
Multi-scale context aggregation by dilated convolutions
Yu, F., & Koltun, V. (2015) · arXiv · doi:10.48550/arxiv.1511.07122 · Cited by 7,738
The Pascal Visual Object Classes Challenge: A Retrospective
Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-014-0733-5 · Cited by 7,139
Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
Krähenbühl, P., & Koltun, V. (2012) · arXiv · doi:10.48550/arxiv.1210.5644 · Cited by 2,908
Flagged first, then by citations
Research works printed but not linked: 12
- [4][4] X. Bai, J. Wang, and G. Sapiro. Dynamic color flow: A motion-adaptive color model for object segmentation in video. In ECCV , 2010. -
- [5][5] L. Bertinetto, J. Valmadre, J. F. Henriques, A. Vedaldi, and P. H. S. Torr. Fullyconvolutional siamese networks for object tracking. In ECCV Workshops , 2016. -
- [6][6] M. Braham and M. Van Droogenbroeck. Deep background subtraction with scenespecific convolutional neural networks. In Int. Conf. on Systems, Signals and Image Proc. , 2016. -
- [7][7] S. Caelles, K.-K. Maninis, J. Pont-Tuset, L. Leal-Taixé, D. Cremers, and L. Van Gool. One-shot video object segmentation. In CVPR , 2017. -
- [8][8] J. Chang, D. Wei, and J. W. Fisher III. A video representation using temporal superpixels. In CVPR , 2013. -
- [12][12] Q. Fan, F. Zhong, D. Lischinski, D. Cohen-Or, and B. Chen. Jumpcut: Non-successive mask transfer and interpolation for video cutout. SIGGRAPH Asia , 34(6), 2015. -
- [15][15] M. Grundmann, V. Kwatra, M. Han, and I. Essa. Efficient hierarchical graph-based video segmentation. In CVPR , 2010. -
- [21][21] S. D. Jain, B. Xiong, and K. Grauman. Pixel objectness. arXiv preprint arXiv:1701.05349 , 2017. - · arXiv
- [23][23] Z. Kalal, K. Mikolajczyk, and J. Matas. Tracking-learning-detection. PAMI , 34(7): 1409-1422, 2012. -
- [24][24] A. Khoreva, R. Benenson, E. Ilg, T. Brox, and B. Schiele. Lucid data dreaming for object tracking. In arXiv preprint arXiv: 1703.09554 , 2017. - · arXiv
- [32][32] K. E. Papoutsakis and A. A. Argyros. Integrating tracking with fine object segmentation. Image and Vision Computing , 31(10):771-785, 2013. -
- [41][41] Y-H. Tsai, M-H. Yang, and M. J. Black. Video segmentation via object flow. In CVPR , 2016. -
Cited by
Sorted by citations
Items 401–446 of 446
Simultaneously Predicting Video Object Segmentation and Optical Flow Without Motion Annotations
Cheng, J., Wang, S., & Zhang, C. (2021) · Communications in computer and information science · doi:10.1007/978-981-16-7189-0_9
Object Segmentation based on Anti-disturb Network in Ecological Surveillance
Dai, W., Wang, H., Fan, C., & Xin, Y. (2021) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc52312.2021.9602523
Learning Memory Propagation And Matching For Semi-Supervised Video Object SegmentationPreprint
Wang, J., Xu, H., Fan, K., Yin, H., & Xia, L. (2022) · Research Square · doi:10.21203/rs.3.rs-1218266/v1
Attention-Guided Memory Model for Video Object Segmentation
Lin, Y., & Tan, Y. (2022) · Communications in computer and information science · doi:10.1007/978-981-19-1253-5_6
Boosting Video Object Segmentation Based on Scale Inconsistency
Wang, H., & Oh, C. (2022) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme52920.2022.9859938
Video Object Segmentation Based on Guided Feature Transfer Learning
Fiaz, M., Mahmood, A., Farooq, S. S., Ali, K., Shaheryar, M., & Jung, S. K. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-06381-7_14
A Semi-supervised Video Object Segmentation Method Based on Adaptive Memory Module
Yang, S., Luo, Z., Cao, D., Lin, D., Su, S., & Li, S. (2022) · Communications in computer and information science · doi:10.1007/978-981-19-4546-5_34
An NLP approach to Image Analysis
Martinez, G. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/kfbi5107
High-Fidelity Face Swapping with Style Blending
Yang, X., Guo, Z., Xue, M., & Shi, Z. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/kupa8487
Pre- and Post-Operative Analysis of Planar Radiographs in Total Hip Replacement
Denton, O., Madden-McKee, C., Hill, J., Beverland, D., Dunne, N., & Lennon, A. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/exjl3727
A Data Augmentation and Pre-processing Technique for Sign Language Fingerspelling Recognition
Fowley, F., Rushe, E., & Ventresque, A. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/xbav3102
Ryan, F., Hu, F., Dietlmeier, J., O'Connor, N. E., & McGuinness, K. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/rtns4233
Acoustic Source Localization Using Straight Line Approximations
Bagchi, S., & de Fréin, R. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/ljrb7078
Detection and Isolation of 3D Objects in Unstructured Environments
Couto, D. D., Butterfield, J., Murphy, A., Rafferty, K., & Coleman, J. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/afmz9460
Asymmetric Label Propagation for Video Object Segmentation
Chen, Z., Yang, M., & Zhang, S. S. (2022) · ACM International Conference on Multimedia in Asia (MMAsia) · doi:10.1145/3551626.3564943
A Compressive Prior Guided Mask Predictive Coding Approach for Video Analysis
Huang, Z., Jia, C., Wang, S., & Ma, S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26316-3_28
Segmentation of Motion Objects in Video Frames using Deep Learning
Jiang, F. X., Jiao, L. I. U., & Jiya, T. I. A. N. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0140902
Semi-supervised Video Object Segmentation based on External Memory Attention
Kim, J., & Hong, S. (2023) · Journal of Broadcast Engineering · doi:10.5909/jbe.2023.28.5.613
Unsupervised Temporal Correspondence Learning for Unified Video Object Removal
Wang, Z., Wang, J., Li, X., Li, Y., Lu, Y., & Wang, S. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3340605 · Cited by 1
Xu, N., Lin, W., Lu, X., & Wei, Y. (2023) · Synthesis lectures on computer vision · doi:10.1007/978-3-031-44656-6_2
Iterative segmentation and propagation based interactive video object segmentation
Luo, S., Yang, S., & Yuan, X. (2024) · International Conference on Algorithm, Image Processing and Machine Vision (AIPMV) · doi:10.1117/12.3014487
Deformable Correlation Networks for Aerial Object Tracking and Segmentation
Xing, D., & Tzes, A. P. (2024) · IntechOpen eBooks · doi:10.5772/intechopen.1003777
Kernel based local matching network for video object segmentation
Wang, G., Li, L., Zhu, M., Zhao, R., & Zhang, X. (2024) · Machine Vision and Applications · doi:10.1007/s00138-024-01524-4
Video object segmentation via couple streams and feature memory
Liang, Y., Xiao, X., Qiu, S., Zhang, Y., & Su, Z. (2024) · IET Image Processing · doi:10.1049/ipr2.13051
Fan, J., Hu, S., Wang, L., Zhang, K., & Лю, Б. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105208
Training Sequential CAG Segmentation Models Without Labeled CAG Video Data
Yao, T.-C., Lin, K.-W., Lee, C., Tseng, P., & Lee, C. (2024) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac61105.2024.00129
MoBox: Enhancing Video Object Segmentation With Motion-Augmented Box Supervision
Li, X., Wang, Q., Li, D., Ge, M., Jia, X., He, Y., & Lu, H. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3451981
Vujasinović, S., Becker, S., Bullinger, S., Scherer‐Negenborn, N., Arens, M., & Stiefelhagen, R. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0901-7_24
Graph Neural Networks for Video Object Segmentation
Liu, W., Hao, H., Wang, H., Zou, Z., & Xing, W. (2024) · Springer eBooks · doi:10.1007/978-981-97-9933-6_7
SKVOS: Sketch-Based Video Object Segmentation with a Large-Scale Benchmark
Yang, R., Li, D., Hu, C., & Zhang, H. (2025) · Applied Sciences · doi:10.3390/app15041751
CAMVOS: Leveraging Context and Memory for Advanced Video Object Segmentation
Arshid, W., Awrangjeb, M., Liew, A. W., & Gao, Y. (2024) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta63115.2024.00098
Low-Shot Video Object Segmentation
Yan, K., Wei, F., Dai, S., Wu, M., Wang, P., & Xu, C. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3552779
Zhang, T., & Yuan, X. (2025) · Journal of Electronic Imaging · doi:10.1117/1.jei.34.2.023047
Granularity-Aware Segment and Track for Click Video Object Segmentation
Liu, C., Fang, R., Pang, C., & Zeng, X. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-9949-0_13
Tzu-Chiao, L., & Lee, S. (2025) · International Conference on Machine Learning and Human-Computer Interaction (MLHMI) · doi:10.1109/mlhmi66056.2025.00014
Gupta, D., Bhatia, K., & Kumar, M. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-4170-3_28
Learning Precise Mask Representation for Siamese Visual Tracking
Yang, P., Hu, F., Wang, Q., & Dou, L. (2025) · Sensors · doi:10.3390/s25185743
Wang, D., Xing, G., & Liu, Y. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755091
Towards Open-World Video Segmentation via Iterative Automatic Prompting
Li, L., Li, X., Jiang, Z., Cheng, X., Niu, Z., Wang, B., & Li, G. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227195
Video inpainting and semi-supervised object removal
Le, T. T. (2019) · theses.fr (ABES) · doi:10.70675/1cf4c899z91b9z479czafcdz6dce7a3b070a
2D and 3D Geometric Attributes Estimation in Images via deep learning
Qiu, X. (2021) · theses.fr (ABES) · doi:10.70675/8a226ac9zb095z400fzbc92zf6461be079a6
Controlling Prediction Dynamics for Reliable Intraoperative Segmentation
Zhou, J., Li, X., Zhang, Y., Peng, X., Qu, W., Ma, P., & Gu, Y. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3818851
SAM2TAP: Repurposing Pre-trained Video Object Segmentation Models for Point Tracking
Zhao, Z., Wang, W., Zhao, C., Fu, Z., Zhouhang, Z., Zhou, J., Chen, H., He, T., & Shen, C. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-026-03045-1
Automated Geomechanical Interpretation of Borehole Images Using Context-Aware AI Mechanism
Awada, M., Cherif, Y. L., Escobar, A. J., & Baho, I. (2026) · SPE Annual Technical Conference and Exhibition · doi:10.2118/234195-ms
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 ›
Are you an author, or do you see a problem with this page? Request a review
