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Modern video segmentation methods adopt object queries to perform inter-frame association and demonstrate satisfactory performance in tracking continuously appearing objects despite large-scale motion and transient occlusion.
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Wu, J., Cao, J., Song, L., Wang, Y., Yang, M., Yuan, J.: Track to detect and segment: An online multi-object tracker. In: CVPR (2021)
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Ying, K., Zhong, Q., Mao, W., Wang, Z., Chen, H., Wu, L.Y., Liu, Y., Fan, C., Zhuge, Y., Shen, C.: Ctvis: Consistent training for online video instance segmentation. In: ICCV (2023)
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Li, M., Li, S., Zhang, X., Zhang, L.: Univs: Unified and universal video segmentation with prompts as queries (2024)
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Li, X., Yuan, H., Li, W., Ding, H., Wu, S., Zhang, W., Li, Y., Chen, K., Loy, C.C.: Omg-seg: Is one model good enough for all segmentation? CVPR (2024)
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Wu, J., Li, X., Xu, S., Yuan, H., Ding, H., Yang, Y., Li, X., Zhang, J., Tong, Y., Jiang, X., Ghanem, B., Tao, D.: Towards open vocabulary learning: A survey. T-PAMI (2024)
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Xu, S., Yuan, H., Shi, Q., Qi, L., Wang, J., Yang, Y., Li, Y., Chen, K., Tong, Y., Ghanem, B., Li, X., Yang, M.H.: Rap-sam:towards real-time all-purpose segment anything. arXiv preprint (2024)
2024
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