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This paper focuses on developing a more effective method of hierarchical propagation for semi-supervised Video Object Segmentation (VOS).
Badrinarayanan, V., Galasso, F., Cipolla, R.: Label propagation in video sequences. In: CVPR. pp. 3265–3272. IEEE (2010)
2010
Earlier work this paper cites.
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. IJCV 88
2010
Earlier work this paper cites.
Hariharan, B., Arbeláez, P., Bourdev, L., Maji, S., Malik, J.: Semantic contours from inverse detectors. In: ICCV. pp. 991–998. IEEE (2011)
2011
Earlier work this paper cites.
Vijayanarasimhan, S., Grauman, K.: Active frame selection for label propagation in videos. In: ECCV. pp. 496–509. Springer (2012)
2012
Earlier work this paper cites.
Avinash Ramakanth, S., Venkatesh Babu, R.: Seamseg: Video object segmentation using patch seams. In: CVPR. pp. 376–383 (2014)
2014
Earlier work this paper cites.
Cheng, M.M., Mitra, N.J., Huang, X., Torr, P.H., Hu, S.M.: Global contrast based salient region detection. TPAMI 37
2014
Earlier work this paper cites.
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: ECCV. pp. 740–755. Springer (2014)
2014
Earlier work this paper cites.
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: ICLR (2015)
2015
Earlier work this paper cites.
Shi, J., Yan, Q., Xu, L., Jia, J.: Hierarchical image saliency detection on extended cssd. TPAMI 38
2015
Earlier work this paper cites.
Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. In: NIPS Workshops (2016)
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)
2016
Earlier work this paper cites.
Perazzi, F., Pont-Tuset, J., McWilliams, B., Van Gool, L., Gross, M., Sorkine-Hornung, A.: A benchmark dataset and evaluation methodology for video object segmentation. In: CVPR. pp. 724–732 (2016)
2016
Earlier work this paper cites.
Caelles, S., Maninis, K.K., Pont-Tuset, J., Leal-Taixé, L., Cremers, D., Van Gool, L.: One-shot video object segmentation. In: CVPR. pp. 221–230 (2017)
2017
Earlier work this paper cites.
Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: CVPR. pp. 1251–1258 (2017)
2017
Earlier work this paper cites.
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: CVPR. pp. 2117–2125 (2017)
2017
Earlier work this paper cites.
Perazzi, F., Khoreva, A., Benenson, R., Schiele, B., Sorkine-Hornung, A.: Learning video object segmentation from static images. In: CVPR. pp. 2663–2672 (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: NIPS (2017)
2017
Earlier work this paper cites.
Voigtlaender, P., Leibe, B.: Online adaptation of convolutional neural networks for video object segmentation. In: BMVC (2017)
2017
Earlier work this paper cites.
Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: ECCV. pp. 801–818 (2018)
2018
Earlier work this paper cites.
Chen, Y., Pont-Tuset, J., Montes, A., Van Gool, L.: Blazingly fast video object segmentation with pixel-wise metric learning. In: CVPR. pp. 1189–1198 (2018)
2018
Earlier work this paper cites.
Elfwing, S., Uchibe, E., Doya, K.: Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. Neural Networks 107
2018
Cited alongside, same era.
Hu, Y.T., Huang, J.B., Schwing, A.G.: Videomatch: Matching based video object segmentation. In: ECCV. pp. 54–70 (2018)
2018
Cited alongside, same era.
Luiten, J., Voigtlaender, P., Leibe, B.: Premvos: Proposal-generation, refinement and merging for video object segmentation. In: ACCV. pp. 565–580 (2018)
2018
Cited alongside, same era.
Parmar, N., Vaswani, A., Uszkoreit, J., Kaiser, L., Shazeer, N., Ku, A., Tran, D.: Image transformer. In: ICCV. pp. 4055–4064. PMLR (2018)
2018
Cited alongside, same era.
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv2: Inverted residuals and linear bottlenecks. In: CVPR. pp. 4510–4520 (2018)
2018
Cheng, H.K., Tai, Y.W., Tang, C.K.: Rethinking space-time networks with improved memory coverage for efficient video object segmentation. In: NeurIPS (2021)
2021
Later among the works it cites.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. In: ICLR (2021)
2021
Later among the works it cites.
Duke, B., Ahmed, A., Wolf, C., Aarabi, P., Taylor, G.W.: Sstvos: Sparse spatiotemporal transformers for video object segmentation. In: CVPR (2021)
2021
Later among the works it cites.
Liu, H., Dai, Z., So, D., Le, Q.V.: Pay attention to mlps. In: NeurIPS. vol. 34, pp. 9204–9215 (2021)
2021
Later among the works it cites.
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: ICCV (2021)
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Cited alongside, same era.
Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: CVPR. pp. 7794–7803 (2018)
2018
Cited alongside, same era.
Wug Oh, S., Lee, J.Y., Sunkavalli, K., Joo Kim, S.: Fast video object segmentation by reference-guided mask propagation. In: CVPR. pp. 7376–7385 (2018)
2018
Cited alongside, same era.
Xiao, H., Feng, J., Lin, G., Liu, Y., Zhang, M.: Monet: Deep motion exploitation for video object segmentation. In: CVPR. pp. 1140–1148 (2018)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Yang, L., Wang, Y., Xiong, X., Yang, J., Katsaggelos, A.K.: Efficient video object segmentation via network modulation. In: CVPR. pp. 6499–6507 (2018)
2018
Cited alongside, same era.
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. In: NAACL. pp. 4171––4186 (2019)
2019
Cited alongside, same era.
Oh, S.W., Lee, J.Y., Xu, N., Kim, S.J.: Video object segmentation using space-time memory networks. In: ICCV (2019)
2019
Cited alongside, same era.
2021
Later among the works it cites.
2021
Later among the works it cites.
Seong, H., Oh, S.W., Lee, J.Y., Lee, S., Lee, S., Kim, E.: Hierarchical memory matching network for video object segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 12889–12898 (2021)
2021
Later among the works it cites.
Vaswani, A., Ramachandran, P., Srinivas, A., Parmar, N., Hechtman, B., Shlens, J.: Scaling local self-attention for parameter efficient visual backbones. In: CVPR. pp. 12894–12904 (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
Wang, Y., Xu, Z., Wang, X., Shen, C., Cheng, B., Shen, H., Xia, H.: End-to-end video instance segmentation with transformers. In: CVPR. pp. 8741–8750 (2021)
2021
Later among the works it cites.
Yan, B., Zhang, X., Wang, D., Lu, H., Yang, X.: Alpha-refine: Boosting tracking performance by precise bounding box estimation. In: CVPR. pp. 5289–5298 (2021)
2021
Later among the works it cites.
Yang, Z., Wei, Y., Yang, Y.: Associating objects with transformers for video object segmentation. In: NeurIPS (2021)
2021
Later among the works it cites.
Yang, Z., Wei, Y., Yang , Y.: Collaborative video object segmentation by multi-scale foreground-background integration. TPAMI (2021)
2021
Later among the works it cites.
Yang, Z., Zhang, J., Wang, W., Han, W., Yu, Y., Li, Y., Wang, J., Wei, Y., Sun, Y., Yang, Y.: Towards multi-object association from foreground-background integration. In: CVPR Workshops (2021)
2021
Later among the works it cites.
Cui, Y., Cheng, J., Wang, L., Wu, G.: Mixformer: End-to-end tracking with iterative mixed attention. In: CVPR (2022)
2022
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Liang, C., Wang, W., Zhou, T., Yang, Y.: Visual abductive reasoning. In: CVPR. pp. 15565–15575 (June 2022)
2022
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Pan, X., Li, P., Yang, Z., Zhou, H., Zhou, C., Yang, H., Zhou, J., Yang, Y.: In-n-out generative learning for dense unsupervised video segmentation. In: ACM MM (2022)
2022
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Xu, X., Wang, J., Li, X., Lu, Y.: Reliable propagation-correction modulation for video object segmentation. In: AAAI (2022)
2022
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Zhu, F., Yang, Z., Yu, X., Yang, Y., Wei, Y.: Instance as identity: A generic online paradigm for video instance segmentation. In: ECCV (2022)
2022
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