  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 .
