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Current semi-supervised video object segmentation (VOS) methods usually leverage the entire features of one frame to predict object masks and update memory.
B. D. Lucas, T. Kanade et al. , “An iterative image registration technique with an application to stereo vision,” in Image Underst. Workshop . Vancouver, 1981, pp. 121–130
1981
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput. , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
I. Budvytis, V. Badrinarayanan, and R. Cipolla, “Label propagation in complex video sequences using semi-supervised learning.” in Proc. Brit. Mach. Vis. Conf. (BMVC) , vol. 2257. Citeseer, 2010, pp. 2258–2259
2010
Earlier work this paper cites.
2014
Earlier work this paper cites.
J. Weston, S. Chopra, and A. Bordes, “Memory networks,” arXiv preprint arXiv:1410.3916 , 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
L. Chen, J. Shen, W. Wang, and B. Ni, “Video object segmentation via dense trajectories,” IEEE Trans. Multimedia , vol. 17, no. 12, pp. 2225–2234, 2015
2015
Earlier work this paper cites.
J. Shi, Q. Yan, L. Xu, and J. Jia, “Hierarchical image saliency detection on extended cssd,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 38, no. 4, pp. 717–729, 2015
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2016, pp. 770–778
2016
Earlier work this paper cites.
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 Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2016, pp. 724–732
2016
Earlier work this paper cites.
S. Caelles, K.-K. Maninis, J. Pont-Tuset, L. Leal-Taixé, D. Cremers, and L. Van Gool, “One-shot video object segmentation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2017, pp. 221–230
2017
Earlier work this paper cites.
P. Voigtlaender and B. Leibe, “Online adaptation of convolutional neural networks for video object segmentation,” in Proc. Brit. Mach. Vis. Conf. (BMVC) , 2017
2017
Earlier work this paper cites.
F. Perazzi, A. Khoreva, R. Benenson, B. Schiele, and A. Sorkine-Hornung, “Learning video object segmentation from static images,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2017, pp. 2663–2672
2017
Earlier work this paper cites.
J. Shin Yoon, F. Rameau, J. Kim, S. Lee, S. Shin, and I. So Kweon, “Pixel-level matching for video object segmentation using convolutional neural networks,” in Proc. Int. Conf. Comput. Vis. (ICCV) , 2017, pp. 2167–2176
2017
Earlier work this paper cites.
L. Wang, H. Lu, Y. Wang, M. Feng, D. Wang, B. Yin, and X. Ruan, “Learning to detect salient objects with image-level supervision,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2017, pp. 136–145
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
J. Cheng, Y.-H. Tsai, W.-C. Hung, S. Wang, and M.-H. Yang, “Fast and accurate online video object segmentation via tracking parts,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2018, pp. 7415–7424
2018
Earlier work this paper cites.
H. Xiao, J. Feng, G. Lin, Y. Liu, and M. Zhang, “Monet: Deep motion exploitation for video object segmentation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2018, pp. 1140–1148
2018
Earlier work this paper cites.
K.-K. Maninis, S. Caelles, Y. Chen, J. Pont-Tuset, L. Leal-Taixé, D. Cremers, and L. Van Gool, “Video object segmentation without temporal information,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 41, no. 6, pp. 1515–1530, 2018
2018
Earlier work this paper cites.
J. Luiten, P. Voigtlaender, and B. Leibe, “Premvos: Proposal-generation, refinement and merging for video object segmentation,” in Proc. Asian Conf. Comput. Vis. (ACCV) . Springer, 2018, pp. 565–580
2018
Earlier work this paper cites.
S. W. Oh, J.-Y. Lee, K. Sunkavalli, and S. J. Kim, “Fast video object segmentation by reference-guided mask propagation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2018, pp. 7376–7385
2018
Earlier work this paper cites.
Y. Chen, J. Pont-Tuset, A. Montes, and L. Van Gool, “Blazingly fast video object segmentation with pixel-wise metric learning,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2018, pp. 1189–1198
2018
Earlier work this paper cites.
Y.-T. Hu, J.-B. Huang, and A. G. Schwing, “Videomatch: Matching based video object segmentation,” in Proc. Eur. Conf. Comput. Vis. (ECCV) , 2018, pp. 54–70
2018
Earlier work this paper cites.
X. Li and C. C. Loy, “Video object segmentation with joint re-identification and attention-aware mask propagation,” in Proc. Eur. Conf. Comput. Vis. (ECCV) , 2018, pp. 90–105
2018
Earlier work this paper cites.
S. Woo, J. Park, J.-Y. Lee, and I. S. Kweon, “Cbam: Convolutional block attention module,” in Proc. Eur. Conf. Comput. Vis. (ECCV) , 2018, pp. 3–19
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
S. W. Oh, J.-Y. Lee, N. Xu, and S. J. Kim, “Video object segmentation using space-time memory networks,” in Proc. Int. Conf. Comput. Vis. (ICCV) , 2019, pp. 9226–9235
2019
Cited alongside, same era.
Q. Wang, L. Zhang, L. Bertinetto, W. Hu, and P. H. Torr, “Fast online object tracking and segmentation: A unifying approach,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2019, pp. 1328–1338
2019
Cited alongside, same era.
S. Xu, D. Liu, L. Bao, W. Liu, and P. Zhou, “Mhp-vos: Multiple hypotheses propagation for video object segmentation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2019, pp. 314–323
2019
Cited alongside, same era.
A. Khoreva, R. Benenson, E. Ilg, T. Brox, and B. Schiele, “Lucid data dreaming for video object segmentation,” Int. J. Comput. Vis. , vol. 127, no. 9, pp. 1175–1197, 2019
2019
Cited alongside, same era.
H. Seong, J. Hyun, and E. Kim, “Kernelized memory network for video object segmentation,” in Proc. Eur. Conf. Comput. Vis. (ECCV) , 2020, pp. 629–645
2020
Later among the works it cites.
M. Sun, J. Xiao, E. G. Lim, B. Zhang, and Y. Zhao, “Fast template matching and update for video object tracking and segmentation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2020, pp. 10 791–10 799
2020
Later among the works it cites.
H. K. Cheng, J. Chung, Y.-W. Tai, and C.-K. Tang, “Cascadepsp: Toward class-agnostic and very high-resolution segmentation via global and local refinement,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2020, pp. 8890–8899
2020
Later among the works it cites.
X. Li, T. Wei, Y. P. Chen, Y.-W. Tai, and C.-K. Tang, “Fss-1000: A 1000-class dataset for few-shot segmentation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2020, pp. 2869–2878
2020
Later among the works it cites.
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K. Duarte, Y. S. Rawat, and M. Shah, “Capsulevos: Semi-supervised video object segmentation using capsule routing,” in Proc. Int. Conf. Comput. Vis. (ICCV) , 2019, pp. 8480–8489
2019
Cited alongside, same era.
Z. Wang, J. Xu, L. Liu, F. Zhu, and L. Shao, “Ranet: Ranking attention network for fast video object segmentation,” in Proc. Int. Conf. Comput. Vis. (ICCV) , 2019, pp. 3978–3987
2019
Cited alongside, same era.
C. Ventura, M. Bellver, A. Girbau, A. Salvador, F. Marques, and X. Giro-i Nieto, “Rvos: End-to-end recurrent network for video object segmentation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2019, pp. 5277–5286
2019
Cited alongside, same era.
J. Johnander, M. Danelljan, E. Brissman, F. S. Khan, and M. Felsberg, “A generative appearance model for end-to-end video object segmentation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2019, pp. 8953–8962
2019
Cited alongside, same era.
S. Sun, N. Akhtar, H. Song, A. Mian, and M. Shah, “Deep affinity network for multiple object tracking,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 43, no. 1, pp. 104–119, 2019
2019
Cited alongside, same era.
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 Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2019, pp. 9481–9490
2019
Cited alongside, same era.
Y. Zeng, P. Zhang, J. Zhang, Z. Lin, and H. Lu, “Towards high-resolution salient object detection,” in Proc. Int. Conf. Comput. Vis. (ICCV) , 2019, pp. 7234–7243
2019
Cited alongside, same era.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al. , “Pytorch: An imperative style, high-performance deep learning library,” Proc. Adv. Neural Inf. Process. Syst. (NIPS) , vol. 32, 2019
2019
Cited alongside, same era.
X. Lu, W. Wang, M. Danelljan, T. Zhou, J. Shen, and L. Van Gool, “Video object segmentation with episodic graph memory networks,” in Proc. Eur. Conf. Comput. Vis. (ECCV) , 2020, pp. 661–679
2020
Later among the works it cites.
G. Bhat, F. J. Lawin, M. Danelljan, A. Robinson, M. Felsberg, L. Van Gool, and R. Timofte, “Learning what to learn for video object segmentation,” in Proc. Eur. Conf. Comput. Vis. (ECCV) , 2020, pp. 777–794
2020
Later among the works it cites.
Z. Yang and Y, Wei and Y. Yang, “Associating objects with transformers for video object segmentation,” Proc. Adv. Neural Inf. Process. Syst. (NIPS) , vol. 34, pp. 2491–2502, 2021
2021
Later among the works it cites.
H. K. Cheng, Y.-W. Tai, and C.-K. Tang, “Rethinking space-time networks with improved memory coverage for efficient video object segmentation,” Proc. Adv. Neural Inf. Process. Syst. (NIPS) , vol. 34, pp. 11 781–11 794, 2021
2021
Later among the works it cites.
S. Liang, X. Shen, J. Huang, and X.-S. Hua, “Video object segmentation with dynamic memory networks and adaptive object alignment,” in Proc. Int. Conf. Comput. Vis. (ICCV) , 2021, pp. 8065–8074
2021
Later among the works it cites.
H. Xie, H. Yao, S. Zhou, S. Zhang, and W. Sun, “Efficient regional memory network for video object segmentation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2021, pp. 1286–1295
2021
Later among the works it cites.
2021
Later among the works it cites.
G.-P. Ji, K. Fu, Z. Wu, D.-P. Fan, J. Shen, and L. Shao, “Full-duplex strategy for video object segmentation,” in Proc. Int. Conf. Comput. Vis. (ICCV) , 2021, pp. 4922–4933
2021
Later among the works it cites.
H. Park, J. Yoo, S. Jeong, G. Venkatesh, and N. Kwak, “Learning dynamic network using a reuse gate function in semi-supervised video object segmentation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2021, pp. 8405–8414
2021
Later among the works it cites.
B. Duke, A. Ahmed, C. Wolf, P. Aarabi, and G. W. Taylor, “Sstvos: Sparse spatiotemporal transformers for video object segmentation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2021, pp. 5912–5921
2021
Later among the works it cites.
L. Hu, P. Zhang, B. Zhang, P. Pan, Y. Xu, and R. Jin, “Learning position and target consistency for memory-based video object segmentation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2021, pp. 4144–4154
2021
Later among the works it cites.
J. Wu, J. Cao, L. Song, Y. Wang, M. Yang, and J. Yuan, “Track to detect and segment: An online multi-object tracker,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2021, pp. 12 352–12 361
2021
Later among the works it cites.
2021
Later among the works it cites.
Z. Yang, Y. Wei, and Y. Yang, “Collaborative video object segmentation by multi-scale foreground-background integration,” IEEE Trans. Pattern Anal. Mach. Intell. , 2021
2021
Later among the works it cites.
H. Wang, X. Jiang, H. Ren, Y. Hu, and S. Bai, “Swiftnet: Real-time video object segmentation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2021, pp. 1296–1305
2021
Later among the works it cites.
Y. Mao, N. Wang, W. Zhou, and H. Li, “Joint inductive and transductive learning for video object segmentation,” in Proc. Int. Conf. Comput. Vis. (ICCV) , 2021, pp. 9670–9679
2021
Later among the works it cites.
H. Seong, S. W. Oh, J.-Y. Lee, S. Lee, S. Lee, and E. Kim, “Hierarchical memory matching network for video object segmentation,” in Proc. Int. Conf. Comput. Vis. (ICCV) , 2021, pp. 12 889–12 898
2021
Later among the works it cites.
H. K. Cheng, Y.-W. Tai, and C.-K. Tang, “Modular interactive video object segmentation: Interaction-to-mask, propagation and difference-aware fusion,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2021, pp. 5559–5568
2021
Later among the works it cites.
W. Ge, X. Lu, and J. Shen, “Video object segmentation using global and instance embedding learning,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2021, pp. 16 836–16 845
2021
Later among the works it cites.
B. Miao, M. Bennamoun, Y. Gao, and A. Mian, “Self-supervised video object segmentation by motion-aware mask propagation,” in Proc. IEEE Int. Conf. Multimedia Expo. (ICME) , 2022
2022
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L. Hong, W. Zhang, L. Chen, W. Zhang, and J. Fan, “Adaptive selection of reference frames for video object segmentation,” IEEE Trans. Image Processing , vol. 31, pp. 1057–1071, 2022
2022
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