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Interactive video object segmentation is a crucial video task, having various applications from video editing to data annotating.
F. Perazzi, J. Pont-Tuset, B. McWilliams, L. Van Gool, M. Gross, and A. Sorkine-Hornung, “A benchmark dataset and evaluation methodology for video object segmentation,” in Computer Vision and Pattern Recognition , 2016
2016
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F. Perazzi, A. Khoreva, R. Benenson, B. Schiele, and A. Sorkine-Hornung, “Learning video object segmentation from static images,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 2663–2672
2017
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2017
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2018
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D. Sun, X. Yang, M.-Y. Liu, and J. Kautz, “Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 8934–8943
2018
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Y. Chen, J. Pont-Tuset, A. Montes, and L. Van Gool, “Blazingly fast video object segmentation with pixel-wise metric learning,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 1189–1198
2018
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2018
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W. Wang, X. Lu, J. Shen, D. J. Crandall, and L. Shao, “Zero-shot video object segmentation via attentive graph neural networks,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 9236–9245
2019
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H. Li, G. Chen, G. Li, and Y. Yu, “Motion guided attention for video salient object detection,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 7274–7283
2019
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X. Lu, W. Wang, C. Ma, J. Shen, L. Shao, and F. Porikli, “See more, know more: Unsupervised video object segmentation with co-attention siamese networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 3623–3632
2019
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L. Zhang, Z. Lin, J. Zhang, H. Lu, and Y. He, “Fast video object segmentation via dynamic targeting network,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 5582–5591
2019
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P. Voigtlaender, Y. Chai, F. Schroff, H. Adam, B. Leibe, and L.-C. Chen, “Feelvos: Fast end-to-end embedding learning for video object segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 9481–9490
2019
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S. W. Oh, J.-Y. Lee, N. Xu, and S. J. Kim, “Video object segmentation using space-time memory networks,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 9226–9235
2019
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S. W. Oh, J.-Y. Lee, N. Xu, and S. J. Kim, “Fast user-guided video object segmentation by interaction-and-propagation networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 5247–5256
2019
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Y. Heo, Y. Jun Koh, and C.-S. Kim, “Interactive video object segmentation using global and local transfer modules,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XVII 16 . Springer, 2020, pp. 297–313
2020
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J. Miao, Y. Wei, and Y. Yang, “Memory aggregation networks for efficient interactive video object segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 10 366–10 375
2020
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X. Lu, W. Wang, J. Shen, D. Crandall, and J. Luo, “Zero-shot video object segmentation with co-attention siamese networks,” IEEE transactions on pattern analysis and machine intelligence , vol. 44, no. 4, pp. 2228–2242, 2020
2020
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X. Huang, J. Xu, Y.-W. Tai, and C.-K. Tang, “Fast video object segmentation with temporal aggregation network and dynamic template matching,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 8879–8889
2020
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Y. Liang, X. Li, N. Jafari, and J. Chen, “Video object segmentation with adaptive feature bank and uncertain-region refinement,” Advances in Neural Information Processing Systems , vol. 33, pp. 3430–3441, 2020
2020
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——, “Space-time memory networks for video object segmentation with user guidance,” IEEE transactions on pattern analysis and machine intelligence , vol. 44, no. 1, pp. 442–455, 2020
2020
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B. Ye, H. Chang, B. Ma, S. Shan, and X. Chen, “Joint feature learning and relation modeling for tracking: A one-stream framework,” in European Conference on Computer Vision . Springer, 2022, pp. 341–357
2022
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K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick, “Masked autoencoders are scalable vision learners,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 16 000–16 009
2022
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C. Doersch, A. Gupta, L. Markeeva, A. Recasens, L. Smaira, Y. Aytar, J. Carreira, A. Zisserman, and Y. Yang, “Tap-vid: A benchmark for tracking any point in a video,” Advances in Neural Information Processing Systems , vol. 35, pp. 13 610–13 626, 2022
2022
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F. Xie, C. Wang, G. Wang, Y. Cao, W. Yang, and W. Zeng, “Correlation-aware deep tracking,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 8751–8760
2022
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A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly et al. , “An image is worth 16x16 words: Transformers for image recognition at scale,” in International Conference on Learning Representations , 2020
2020
Cited alongside, same era.
Z. Teed and J. Deng, “Raft: Recurrent all-pairs field transforms for optical flow,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part II 16 . Springer, 2020, pp. 402–419
2020
Cited alongside, same era.
H. K. Cheng, Y.-W. Tai, and C.-K. Tang, “Modular interactive video object segmentation: Interaction-to-mask, propagation and difference-aware fusion,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 5559–5568
2021
Cited alongside, same era.
Y. Heo, Y. J. Koh, and C.-S. Kim, “Guided interactive video object segmentation using reliability-based attention maps,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 7322–7330
2021
Cited alongside, same era.
Z. Yang, Y. Wei, and Y. Yang, “Collaborative video object segmentation by multi-scale foreground-background integration,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 44, no. 9, pp. 4701–4712, 2021
2021
Cited alongside, same era.
H. K. Cheng, Y.-W. Tai, and C.-K. Tang, “Rethinking space-time networks with improved memory coverage for efficient video object segmentation,” Advances in Neural Information Processing Systems , vol. 34, pp. 11 781–11 794, 2021
2021
Cited alongside, same era.
Z. Yang, Y. Wei, and Y. Yang, “Associating objects with transformers for video object segmentation,” Advances in Neural Information Processing Systems , vol. 34, pp. 2491–2502, 2021
2021
Cited alongside, same era.
Z. Yin, J. Zheng, W. Luo, S. Qian, H. Zhang, and S. Gao, “Learning to recommend frame for interactive video object segmentation in the wild,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 15 445–15 454
2021
Cited alongside, same era.
Later among the works it cites.
Z. Yang, Q. Shi, and Y. Fang, “Multi-scale deep feature transfer for automatic video object segmentation,” Neural Processing Letters , vol. 55, no. 8, pp. 11 701–11 719, 2023
2023
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2023
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2023
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2023
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2023
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2023
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2023
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2023
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2023
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Y. Cai, J. Liu, J. Tang, and G. Wu, “Robust object modeling for visual tracking,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 9589–9600
2023
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X. Chen, H. Peng, D. Wang, H. Lu, and H. Hu, “Seqtrack: Sequence to sequence learning for visual object tracking,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 14 572–14 581
2023
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L. Ke, M. Ye, M. Danelljan, Y.-W. Tai, C.-K. Tang, F. Yu et al. , “Segment anything in high quality,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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