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Existing image classification models usually classify data collected from terrestrial environments. However, existing image classification models trained with terrestrial data are unsuitable for underwater images, as identifying underwater data is challenging due to their incomplete and noisy features. To address this, we propose a cross-modal augmentation via fusion (\n            <jats:bold>CMAF<\/jats:bold>\n            ) framework for acoustic-based fish image classification. Our approach involves separating the process into two branches: visual modality and sonar signal modality, where the latter provides a complementary character feature. We augment the visual modality, design an attention-based fusion module, and adopt a masking-based training strategy with a mask-based focal loss to improve the learning of local features and address the class imbalance problem. Our proposed method outperforms the state-of-the-art methods. Our source code is available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"url\" xlink:href=\"https:\/\/github.com\/WilkinsYang\/CMAF\">https:\/\/github.com\/WilkinsYang\/CMAF<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3636427","type":"journal-article","created":{"date-parts":[[2023,12,6]],"date-time":"2023-12-06T12:24:13Z","timestamp":1701865453000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["CMAF: Cross-Modal Augmentation via Fusion for Underwater Acoustic Image Recognition"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6949-2609","authenticated-orcid":false,"given":"Shih-Wei","family":"Yang","sequence":"first","affiliation":[{"name":"National Yang Ming Chiao Tung University, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6412-5457","authenticated-orcid":false,"given":"Li-Hsiang","family":"Shen","sequence":"additional","affiliation":[{"name":"National Central University, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2216-077X","authenticated-orcid":false,"given":"Hong-Han","family":"Shuai","sequence":"additional","affiliation":[{"name":"National Yang Ming Chiao Tung University, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2781-8449","authenticated-orcid":false,"given":"Kai-Ten","family":"Feng","sequence":"additional","affiliation":[{"name":"National Yang Ming Chiao Tung University, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,1,11]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"568","volume-title":"Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR\u201917)","author":"Akkaynak D.","year":"2017","unstructured":"D. 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