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The task of semi-supervised video object segmentation (VOS) has been greatly advanced and state-of-the-art performance has been made by dense matching-based methods.
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F. Perazzi, A. Khoreva, R. Benenson, B. Schiele, A.Sorkine-Hornung, Learning video object segmentation from static images, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
2017
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X. Li, Y. Qi, Z. Wang, K. Chen, Z. Liu, J. Shi, P. Luo, X. Tang, C. C. Loy, Video object segmentation with re-identification, in: The 2017 DAVIS Challenge on Video Object Segmentation - CVPR Workshops, 2017
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J. Cheng, Y.-H. Tsai, S. Wang, M.-H. Yang, Segflow: Joint learning for video object segmentation and optical flow, in: Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 686–695
2017
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W.-D. Jang, C.-S. Kim, Online video object segmentation via convolutional trident network, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 5849–5858
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A. Khoreva, R. Benenson, E. Ilg, T. Brox, B. Schiele, Lucid data dreaming for object tracking, in: The 2017 DAVIS Challenge on Video Object Segmentation - CVPR Workshops, 2017
2017
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E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy, T. Brox, Flownet 2.0: Evolution of optical flow estimation with deep networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 2462–2470
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2017
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P. Voigtlaender, B. Leibe, Online adaptation of convolutional neural networks for video object segmentation, in: Proceedings of the British Machine Vision Conference (BMVC), 2017
2017
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S. Caelles, K.-K. Maninis, J. Pont-Tuset, L. Leal-Taixé, D. Cremers, L. Van Gool, One-shot video object segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 221–230
2017
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S. W. Oh, J.-Y. Lee, K. Sunkavalli, S. J. Kim, Fast video object segmentation by reference-guided mask propagation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 7376–7385
2018
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T.-W. Hui, X. Tang, C. C. Loy, Liteflownet: A lightweight convolutional neural network for optical flow estimation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 8981–8989
2018
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N. Xu, L. Yang, Y. Fan, J. Yang, D. Yue, Y. Liang, B. Price, S. Cohen, T. Huang, Youtube-vos: Sequence-to-sequence video object segmentation, in: Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 585–601
Z. Yang, Y. Wei, Y. Yang, Collaborative video object segmentation by foreground-background integration, in: Proceedings of the European Conference on Computer Vision (ECCV), 2020
2020
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A. Robinson, F. J. Lawin, M. Danelljan, F. S. Khan, M. Felsberg, Learning fast and robust target models for video object segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020
2020
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M. Sun, J. Xiao, E. G. Lim, Y. Xie, J. Feng, Adaptive roi generation for video object segmentation using reinforcement learning, Pattern Recognition 106 (2020) 107465
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Y. Li, N. Xu, P. Jinlong, J. See, L. Weiyao, Delving into the cyclic mechanism in semi-supervised video object segmentation, in: Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), 2020
2020
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2018
Cited alongside, same era.
L. Yang, Y. Wang, X. Xiong, J. Yang, A. K. Katsaggelos, Efficient video object segmentation via network modulation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 6499–6507
2018
Cited alongside, same era.
K.-K. Maninis, S. Caelles, Y. Chen, J. Pont-Tuset, L. Leal-Taixé, D. Cremers, L. Van Gool, Video object segmentation without temporal information, IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI) 41 (6) (2018) 1515–1530
2018
Cited alongside, same era.
L. Bao, B. Wu, W. Liu, Cnn in mrf: Video object segmentation via inference in a cnn-based higher-order spatio-temporal mrf, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 5977–5986
2018
Cited alongside, same era.
J. Luiten, P. Voigtlaender, B. Leibe, Premvos: Proposal-generation, refinement and merging for video object segmentation, in: Proceedings of the Asian Conference on Computer Vision (ACCV), Springer, 2018, pp. 565–580
2018
Cited alongside, same era.
S. W. Oh, J.-Y. Lee, N. Xu, S. J. Kim, Video object segmentation using space-time memory networks, in: Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2019, pp. 9226–9235
2019
Cited alongside, same era.
A. Dave, P. Tokmakov, D. Ramanan, Towards segmenting anything that moves, in: Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCVW), 2019
2019
Cited alongside, same era.
Z. Wang, J. Xu, L. Liu, F. Zhu, L. Shao, Ranet: Ranking attention network for fast video object segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 3977–3986
2019
Cited alongside, same era.
H. Li, G. Chen, G. Li, Y. Yu, Motion guided attention for video salient object detection, in: Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2019, pp. 7274–7283
2019
Cited alongside, same era.
X. Lu, W. Wang, D. Martin, T. Zhou, J. Shen, V. G. Luc, Video object segmentation with episodic graph memory networks, in: Proceedings of the European Conference on Computer Vision (ECCV), 2020
2020
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H. Seong, J. Hyun, E. Kim, Kernelized memory network for video object segmentation, in: Proceedings of the European Conference on Computer Vision (ECCV), 2020, pp. 629–645
2020
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Y. Li, Z. Shen, Y. Shan, Fast video object segmentation using the global context module, in: Proceedings of the European Conference on Computer Vision (ECCV), Springer, 2020, pp. 735–750
2020
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Y. Tian, G. Cheng, J. Gelernter, S. Yu, C. Song, B. Yang, Joint temporal context exploitation and active learning for video segmentation, Pattern Recognition 100 (2020) 107158
2020
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2020
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Z. Teed, J. Deng, Raft: Recurrent all-pairs field transforms for optical flow, in: Proceedings of the European Conference on Computer Vision (ECCV), Springer, 2020, pp. 402–419
2020
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T. Zhou, J. Li, S. Wang, R. Tao, J. Shen, Matnet: Motion-attentive transition network for zero-shot video object segmentation, IEEE Transactions on Image Processing (TIP) 29 (2020) 8326–8338
2020
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G. Bhat, F. J. Lawin, M. Danelljan, A. Robinson, M. Felsberg, L. Van Gool, R. Timofte, Learning what to learn for video object segmentation, in: Proceedings of the European Conference on Computer Vision (ECCV), 2020, pp. 777–794
2020
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2020
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Y. Li, Z. Shen, Y. Shan, Fast video object segmentation using the global context module, in: Proceedings of the European Conference on Computer Vision (ECCV), Springer, 2020, pp. 735–750
2020
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Y. Yin, D. Xu, X. Wang, L. Zhang, Agunet: Annotation-guided u-net for fast one-shot video object segmentation, Pattern Recognition 110 (2021) 107580
2021
Closest in time.
Z. Zhao, S. Zhao, J. Shen, Real-time and light-weighted unsupervised video object segmentation network, Pattern Recognition (2021) 108120
2021
Closest in time.
H. Xie, H. Yao, S. Zhou, S. Zhang, W. Sun, Efficient regional memory network for video object segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 1286–1295
2021
Closest in time.
L. Hu, P. Zhang, B. Zhang, P. Pan, Y. Xu, R. Jin, Learning position and target consistency for memory-based video object segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 4144–4154
2021
Closest in time.