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Referring Video Object Segmentation (RVOS) is a challenging task due to its requirement for temporal understanding.
Kazemzadeh, S., Ordonez, V., Matten, M., Berg, T.: Referitgame: Referring to objects in photographs of natural scenes. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). pp. 787–798 (2014)
2014
Earlier work this paper cites.
Kitaev, N., Cao, S., Klein, D.: Multilingual constituency parsing with self-attention and pre-training. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. pp. 3499–3505. Association for Computational Linguistics, Florence, Italy (Jul 2019). https://doi.org/10.18653/v1/P19-1340, https://www.aclweb.org/anthology/P19-1340
2019
Earlier work this paper cites.
Seo, S., Lee, J.Y., Han, B.: Urvos: Unified referring video object segmentation network with a large-scale benchmark. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XV 16. pp. 208–223. Springer (2020)
2020
Earlier work this paper cites.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PMLR (2021)
2021
Earlier work this paper cites.
Ding, H., Liu, C., He, S., Jiang, X., Loy, C.C.: MeViS: A large-scale benchmark for video segmentation with motion expressions. In: ICCV (2023)
2023
Earlier work this paper cites.
Ding, H., Liu, C., He, S., Jiang, X., Torr, P.H., Bai, S.: MOSE: A new dataset for video object segmentation in complex scenes. In: ICCV (2023)
2023
Cited alongside, same era.
Ke, L., Ye, M., Danelljan, M., Liu, Y., Tai, Y.W., Tang, C.K., Yu, F.: Segment anything in high quality. In: NeurIPS (2023)
2023
Cited alongside, same era.
2023
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2024
Cited alongside, same era.
2024
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He, S., Ding, H.: Decoupling static and hierarchical motion perception for referring video segmentation. In: CVPR (2024)
2024
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2024
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2024
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2024
Cited alongside, same era.
Yan, S., Zhang, R., Guo, Z., Chen, W., Zhang, W., Li, H., Qiao, Y., Dong, H., He, Z., Gao, P.: Referred by multi-modality: A unified temporal transformer for video object segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 38, pp. 6449–6457 (2024)
2024
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