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 effectively segment aquatic animals 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. The code is available at https://github.com/lmquan2000/mask-mixup. The dataset is available at https://doi.org/10.5281/zenodo.8208877 .

翻译：近年来，目标分割研究取得了显著进展。除通用物体外，水生动物也引起了研究关注。基于深度学习的方法被广泛用于水生动物的分割任务，并取得了良好的性能。然而，目前仍缺乏具有挑战性的基准数据集。本研究构建了名为水生动物物种（Aquatic Animal Species）的新数据集，并设计了一种新颖的引导式混合增强与多模型融合的水生动物分割方法（GUNNEL），该方法利用多个分割模型的优势实现水生动物的有效分割，并通过合成困难样本来提升训练性能。大量实验证明，所提框架在性能上优于现有最先进的实例分割方法。代码开源地址：https://github.com/lmquan2000/mask-mixup，数据集获取地址：https://doi.org/10.5281/zenodo.8208877。