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Video segmentation -- partitioning video frames into multiple segments or objects -- plays a critical role in a broad range of practical applications, from enhancing visual effects in movie, to understanding scenes in autonomous driving, to creating virtual background in video conferencing.
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D. Liu, Y. Cui, W. Tan, and Y. Chen, “Sg-net: Spatial granularity network for one-stage video instance segmentation,” in
2021
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Y. Wang, Z. Xu, X. Wang, C. Shen, B. Cheng, H. Shen, and H. Xia, “End-to-end video instance segmentation with transformers,” in
2021
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L. Hoyer, D. Dai, Y. Chen, A. Koring, S. Saha, and L. Van Gool, “Three ways to improve semantic segmentation with self-supervised depth estimation,” in
2021
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S. Woo, D. Kim, J.-Y. Lee, and I. S. Kweon, “Learning to associate every segment for video panoptic segmentation,” in
2021
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S. Qiao, Y. Zhu, H. Adam, A. Yuille, and L.-C. Chen, “Vip-deeplab: Learning visual perception with depth-aware video panoptic segmentation,” in
2021
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Q. Liu, V. Ramanathan, D. Mahajan, A. Yuille, and Z. Yang, “Weakly supervised instance segmentation for videos with temporal mask consistency,” in
2021
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Y. Fu, S. Liu, U. Iqbal, S. De Mello, H. Shi, and J. Kautz, “Learning to track instances without video annotations,” in
2021
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H. Lin, R. Wu, S. Liu, J. Lu, and J. Jia, “Video instance segmentation with a propose-reduce paradigm,” in
2021
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S. Yang, Y. Fang, X. Wang, Y. Li, C. Fang, Y. Shan, B. Feng, and W. Liu, “Crossover learning for fast online video instance segmentation,” in
2021
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2021
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S. Liu, T. Hui, S. Huang, Y. Wei, B. Li, and G. Li, “Cross-modal progressive comprehension for referring segmentation,”
2021
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Y. Cui, L. Yan, Z. Cao, and D. Liu, “Tf-blender: Temporal feature blender for video object detection,” in
2021
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J. Miao, Y. Wei, Y. Wu, C. Liang, G. Li, and Y. Yang, “Vspw: A large-scale dataset for video scene parsing in the wild,” in
2021
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W. Wang, M. Feiszli, H. Wang, and D. Tran, “Unidentified video objects: A benchmark for dense, open-world segmentation,” in
2021
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T. Zhou, W. Wang, E. Konukoglu, and L. Van Gool, “Rethinking semantic segmentation: A prototype view,” in
2022
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L. Li, T. Zhou, W. Wang, L. Yang, J. Li, and Y. Yang, “Locality-aware inter-and intra-video reconstruction for self-supervised correspondence learning,” in
2022
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2022
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F. Lin, H. Xie, Y. Li, and Y. Zhang, “Query-memory re-aggregation for weakly-supervised video object segmentation,” in
2046
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J. Chang, D. Wei, and J. W. Fisher, “A video representation using temporal superpixels,” in
2058
Closest in time.