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
Hauptverfasser: Khoshboresh-Masouleh, Mehdi, Shah-Hosseini, Reza
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.
ISSN:2624-9898
2624-9898
DOI:10.3389/fcomp.2022.1062792