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• W4372342354 abstract "Most offline video instance segmentation (VIS) methods lack consideration for multi-scale spatio-temporal features, which leads to unstable instance association across frames. To address this problem, we propose IAST that builds Instance Association relying on Spatio-Temporal features for video instance segmentation. In detail, we design a novel Scale-to-Scale Attention Module in the encoder of IAST, which constructs stable cross-frame instance associations by completely leveraged multi-scale spatio-temporal features. In addition, we introduce a new data augmentation method called Sequential Copy-Paste, which effectively alleviates the overfitting problem caused by insufficient training data and enhances the robustness of the model. Empirically, IAST achieves the state-of-the-art VIS benchmarks with a ResNet-50 backbone: 47.4% AP, 41.6% AP on YouTube-VIS 2019 & 2021. Such achievements significantly outperform the previous state-of-the-art performance of 1.0% at the expense of fewer parameters. Code is available at https://github.com/clozureyez/IAST." @default.
• W4372342354 created "2023-05-07" @default.
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• W4372342354 date "2023-06-04" @default.
• W4372342354 modified "2023-09-27" @default.
• W4372342354 title "IAST: Instance Association Relying on Spatio-Temporal Features for Video Instance Segmentation" @default.
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• W4372342354 doi "https://doi.org/10.1109/icassp49357.2023.10095641" @default.
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