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- W4309733281 abstract "Deep learning-based target segmentation requires a big training dataset to achieve good results. In this regard, few-shot learning a model that quickly adapts to new targets with a few labeled support samples is proposed to tackle this issue. In this study, we introduce a new multimodal few-shot learning [e.g., red-green-blue (RGB), thermal, and depth] for real-time multiple target segmentation in a real-world application with a few examples based on a new squeeze-and-attentions mechanism for multiscale and multiple target segmentation. Compared to the state-of-the-art methods (HSNet, CANet, and PFENet), the proposed method demonstrates significantly better performance on the PST900 dataset with 32 time-series sets in both Hand-Drill, and Survivor classes." @default.
- W4309733281 created "2022-11-29" @default.
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- W4309733281 date "2022-11-22" @default.
- W4309733281 modified "2023-10-14" @default.
- W4309733281 title "Real-time multiple target segmentation with multimodal few-shot learning" @default.
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- W4309733281 doi "https://doi.org/10.3389/fcomp.2022.1062792" @default.
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