GUNNEL: Guided Mixup Augmentation and Multi-Model Fusion for Aquatic Animal Segmentation
This work addresses the lack of challenging datasets for benchmarking aquatic animal segmentation, which is important for researchers in marine biology and computer vision, though it is incremental as it builds on existing deep learning methods.
The authors tackled the problem of aquatic animal segmentation by building a new dataset called 'Aquatic Animal Species' and proposing GUNNEL, a method that uses guided mixup augmentation and multi-model fusion, which demonstrated superiority over existing state-of-the-art instance segmentation methods in experiments.
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. The code is available at https://github.com/lmquan2000/mask-mixup. The dataset is available at https://doi.org/10.5281/zenodo.8208877.