Motion perception-driven multimodal self-supervised video object segmentation
Author:
Funder
National Natural Science Foundation of China Youth Fund
Key Research and Promotion Projects of Henan Province
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00371-024-03597-8.pdf
Reference66 articles.
1. Lian, L., Wu, Z., Yu, S.X.: Bootstrapping objectness from videos by relaxed common fate and visual grouping. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14582–14591 (2023). https://doi.org/10.1109/CVPR52729.2023.01401
2. Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., Joulin, A.: Emerging properties in self-supervised vision transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9650–9660 (2021). https://doi.org/10.1109/ICCV48922.2021.00951
3. Wang, Y., Shen, X., Yuan, Y., Du, Y., Li, M., Hu, S.X., Crowley, J.L., Vaufreydaz, D.: TokenCut: segmenting objects in images and videos with self-supervised transformer and normalized cut. IEEE Trans. Pattern Anal. Mach. Intell. 45(12), 15790–15801 (2023). https://doi.org/10.1109/TPAMI.2023.3305122
4. Li, C., Chen, Z., Sheng, B., Li, P., He, G.: Video flickering removal using temporal reconstruction optimization. Multimed. Tools Appl. 79, 4661–4679 (2020). https://doi.org/10.1007/s11042-019-7413-y
5. Lu, X., Wang, W., Shen, J., Tai, Y.W., Crandall, D.J., Hoi, S.C.: Learning video object segmentation from unlabeled videos. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8960–8970 (2020). arXiv:2003.05020
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