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Many of the recent successful methods for video object segmentation (VOS) are overly complicated, heavily rely on fine-tuning on the first frame, and/or are slow, and are hence of limited practical use.
Efficient video object segmentation via network modulation
L. Yang, Y. Wang, X. Xiong, J. Yang, and A. K. Katsaggelos · 1902
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ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Learning object class detectors from weakly annotated video
A. Prest, C. Leistner, J. Civera, C. Schmid, and V. Ferrari · 2012
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Supervoxel-consistent foreground propagation in video
S. D. Jain and K. Grauman · 2014
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Microsoft COCO: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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Flownet: Learning optical flow with convolutional networks
A. Dosovitskiy, P. Fischery, E. Ilg, P. Hausser, C. Hazirbas, V. Golkov, P. v. d. Smagt, D. Cremers, and T. Brox · 2015
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Batch normalization: accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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A benchmark dataset and evaluation methodology for video object segmentation
F. Perazzi, J. Pont-Tuset, B. McWilliams, L. Van Gool, M. Gross, and A. Sorkine-Hornung · 2016
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Bridging category-level and instance-level semantic image segmentation
Z. Wu, C. Shen, and A. v. d. Hengel · 2016
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One-shot video object segmentation
S. Caelles, K.-K. Maninis, J. Pont-Tuset, L. Leal-Taixé, D. Cremers, and L. Van Gool · 2017
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2017
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Rethinking atrous convolution for semantic image segmentation
L.-C. Chen, G. Papandreou, F. Schroff, and H. Adam · 2017
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Xception: Deep learning with depthwise separable convolutions
F. Chollet · 2017
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Semantic instance segmentation via deep metric learning
A. Fathi, Z. Wojna, V. Rathod, P. Wang, H. O. Song, S. Guadarrama, and K. P. Murphy · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam · 2017
Cited alongside, same era.
Maskrnn: Instance level video object segmentation
Y.-T. Hu, J.-B. Huang, and A. G. Schwing · 2017
Cited alongside, same era.
Lucid data dreaming for object tracking
A. Khoreva, R. Benenson, E. Ilg, T. Brox, and B. Schiele · 2017
Cited alongside, same era.
Learning video object segmentation from static images
F. Perazzi, A. Khoreva, R. Benenson, B. Schiele, and A. Sorkine-Hornung · 2017
Cited alongside, same era.
Full-resolution residual networks for semantic segmentation in street scenes
T. Pohlen, A. Hermans, M. Mathias, and B. Leibe · 2017
Cited alongside, same era.
Video object segmentation by learning location-sensitive embeddings
H. Ci, C. Wang, and Y. Wang · 2018
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Reinforcement cutting-agent learning for video object segmentation
J. Han, L. Yang, D. Zhang, X. Chang, and X. Liang · 2018
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Motion-guided cascaded refinement network for video object segmentation
P. Hu, G. Wang, X. Kong, J. Kuen, and Y.-P. Tan · 2018
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Videomatch: Matching based video object segmentation
Y.-T. Hu, J.-B. Huang, and A. G. Schwing · 2018
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Instance embedding transfer to unsupervised video object segmentation
S. Li, B. Seybold, A. Vorobyov, A. Fathi, Q. Huang, and C.-C. J. Kuo · 2018
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Video object segmentation with joint re-identification and attention-aware mask propagation
X. Li and C. Change Loy · 2018
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J. Pont-Tuset, F. Perazzi, S. Caelles, P. Arbeláez, A. Sorkine-Hornung, and L. Van Gool · 2017
Cited alongside, same era.
Deformable convolutional networks – coco detection and segmentation challenge 2017 entry
H. Qi, Z. Zhang, B. Xiao, H. Hu, B. Cheng, Y. Wei, and J. Dai · 2017
Cited alongside, same era.
Online adaptation of convolutional neural networks for the 2017 DAVIS challenge on video object segmentation
P. Voigtlaender and B. Leibe · 2017
Cited alongside, same era.
Online adaptation of convolutional neural networks for video object segmentation
P. Voigtlaender and B. Leibe · 2017
Cited alongside, same era.
The 2018 DAVIS challenge on video object segmentation
S. Caelles, A. Montes, K.-K. Maninis, Y. Chen, L. Van Gool, F. Perazzi, and J. Pont-Tuset · 2018
Cited alongside, same era.
Encoder-decoder with atrous separable convolution for semantic image segmentation
L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam · 2018
Cited alongside, same era.
Blazingly fast video object segmentation with pixel-wise metric learning
Y. Chen, J. Pont-Tuset, A. Montes, and L. Van Gool · 2018
Cited alongside, same era.
Later among the works it cites.
PReMVOS: Proposal-generation, refinement and merging for the davis challenge on video object segmentation 2018
J. Luiten, P. Voigtlaender, and B. Leibe · 2018
Later among the works it cites.
PReMVOS: Proposal-generation, refinement and merging for the YouTube-VOS challenge on video object segmentation 2018
J. Luiten, P. Voigtlaender, and B. Leibe · 2018
Later among the works it cites.
PReMVOS: Proposal-generation, refinement and merging for video object segmentation
J. Luiten, P. Voigtlaender, and B. Leibe · 2018
Later among the works it cites.
Video object segmentation without temporal information
K.-K. Maninis, S. Caelles, Y. Chen, J. Pont-Tuset, L. L. Taixé, and L. Van Gool · 2018
Later among the works it cites.
Fast video object segmentation by reference-guided mask propagation
S. Wug Oh, J.-Y. Lee, K. Sunkavalli, and S. Joo Kim · 2018
Later among the works it cites.
Monet: Deep motion exploitation for video object segmentation
H. Xiao, J. Feng, G. Lin, Y. Liu, and M. Zhang · 2018
Later among the works it cites.
YouTube-VOS: Sequence-to-sequence video object segmentation
N. Xu, L. Yang, Y. Fan, J. Yang, D. Yue, Y. Liang, B. Price, S. Cohen, and T. Huang · 2018
Later among the works it cites.