GUNNEL: Guided Mixup Augmentation and Multi-Model Fusion for Aquatic Animal Segmentation
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2021
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| _version_ | 1866911028370997248 |
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| author | Le, Minh-Quan Le, Trung-Nghia Nguyen, Tam V. Echizen, Isao Tran, Minh-Triet |
| author_facet | Le, Minh-Quan Le, Trung-Nghia Nguyen, Tam V. Echizen, Isao Tran, Minh-Triet |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2112_06193 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | GUNNEL: Guided Mixup Augmentation and Multi-Model Fusion for Aquatic Animal Segmentation Le, Minh-Quan Le, Trung-Nghia Nguyen, Tam V. Echizen, Isao Tran, Minh-Triet Computer Vision and Pattern Recognition 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. |
| title | GUNNEL: Guided Mixup Augmentation and Multi-Model Fusion for Aquatic Animal Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2112.06193 |