Real-time multiple target segmentation with multimodal few-shot learning

Khoshboresh-Masouleh, Mehdi and Shah-Hosseini, Reza (2022) Real-time multiple target segmentation with multimodal few-shot learning. Frontiers in Computer Science, 4. ISSN 2624-9898

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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.

Item Type: Article
Subjects: European Scholar > Computer Science
Depositing User: Managing Editor
Date Deposited: 23 Dec 2022 04:10
Last Modified: 11 Jul 2024 05:55
URI: http://article.publish4promo.com/id/eprint/381

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