Real-time multiple target segmentation with multimodal few-shot learning
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 lea...
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Veröffentlicht in: | Frontiers in computer science (Lausanne) 2022-11, Vol.4 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | 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. |
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ISSN: | 2624-9898 2624-9898 |
DOI: | 10.3389/fcomp.2022.1062792 |