The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Fast Video Object Segmentation by Reference-Guided Mask Propagation
Abstract
We present an efficient method for the semi-supervised video object segmentation. Our method achieves accuracy competitive with state-of-the-art methods while running in a fraction of time compared to others. To this end, we propose a deep Siamese encoder-decoder network that is designed to take advantage of mask propagation and object detection while avoiding the weaknesses of both approaches. Our network, learned through a two-stage training process that exploits both synthetic and real data, works robustly without any online learning or post-processing. We validate our method on four benchmark sets that cover single and multiple object segmentation. On all the benchmark sets, our method shows comparable accuracy while having the order of magnitude faster runtime. We also provide extensive ablation and add-on studies to analyze and evaluate our framework.
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Cites
Deep Residual Learning for Image Recognition
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Adam: A Method for Stochastic Optimization
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545
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Cited by
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
FEELVOS: Fast End-To-End Embedding Learning for Video Object Segmentation
Voigtlaender, P., Chai, Y., Schroff, F., Adam, H., Leibe, B., & Chen, L.-C. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00971 · Cited by 468
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
PReMVOS: Proposal-Generation, Refinement and Merging for Video Object Segmentation
Luiten, J., Voigtlaender, P., & Leibe, B. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-20870-7_35 · Cited by 282
VideoMatch: Matching Based Video Object Segmentation
Hu, Y.-T., Huang, J.-B., & Schwing, A. G. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01237-3_4 · Cited by 274
Metadata
- Authors
- Oh, S. W., Lee, J.-Y., Sunkavalli, K., & Kim, S. J.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 7376-7385
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2799157347
- Cited by
- 420
- References
- 48
- FWCI
- 15.15
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Oh, S. W., Lee, J.-Y., Sunkavalli, K., & Kim, S. J. (2018). Fast Video Object Segmentation by Reference-Guided Mask Propagation. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2018.00770
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