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<p>Recent years have witnessed great advances in object segmentation research. In addition to generic objects, aquatic animals have attracted research attention. Deep learning-based methods are widely used for aquatic animal segmentation and have achieved promising performance. However, there is a lack of challenging datasets for benchmarking. In this work, we build a new dataset dubbed “Aquatic Animal Species." We also devise a novel <b>GU</b>ided mixup augme<b>N</b>tatio<b>N</b> and multi-mod<b>E</b>l fusion for aquatic anima<b>L</b> segmentation (GUNNEL) that leverages the advantages of multiple segmentation models to segment aquatic animals effectively and improves the training performance by synthesizing hard samples. Extensive experiments demonstrated the superiority of our proposed framework over existing state-of-the-art instance segmentation methods.</p>

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GUNNEL: guided mixup augmentation and multi-model fusion for aquatic animal segmentation

  • 期刊论文

  • 发表日期：

    2025年7月3日

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  • Minh-Quan Le,
  • Trung-Nghia Le,
  • Tam V. Nguyen,
  • Isao Echizen,
  • Minh-Triet Tran

摘要

Recent years have witnessed great advances in object segmentation research. In addition to generic objects, aquatic animals have attracted research attention. Deep learning-based methods are widely used for aquatic animal segmentation and have achieved promising performance. However, there is a lack of challenging datasets for benchmarking. In this work, we build a new dataset dubbed “Aquatic Animal Species." We also devise a novel GUided mixup augmeNtatioN and multi-modEl fusion for aquatic animaL segmentation (GUNNEL) that leverages the advantages of multiple segmentation models to segment aquatic animals effectively and improves the training performance by synthesizing hard samples. Extensive experiments demonstrated the superiority of our proposed framework over existing state-of-the-art instance segmentation methods.