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-viEw fusion for aquatic animaL segmentation (GUNNEL)
that leverages the advantages of multiple view 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