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An image is worth 16 \u00d7 16 words: transformers for image recognition at scale. arXiv:2010.11929. 2020."},{"key":"ref30","doi-asserted-by":"crossref","first-page":"25484","DOI":"10.1007\/s11227-024-06418-w","article-title":"Arbitrary shape text detection fusing InceptionNeXt and multi-scale attention mechanism","volume":"80","author":"Li","year":"2024","journal-title":"J Supercomput"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"127432","DOI":"10.1016\/j.neucom.2024.127432","article-title":"AudioRepInceptionNeXt: a lightweight single-stream architecture for efficient audio recognition","volume":"578","author":"Lau","year":"2024","journal-title":"Neurocomputing"},{"key":"ref32","doi-asserted-by":"crossref","unstructured":"Lin T. Focal loss for dense object detection. arXiv:1708.02002. 2017.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref33","unstructured":"Kang R, Liatsis P, Kyritsis DC. Flame-state monitoring based on very low number of visible or infrared images via few-shot learning. arXiv:2210.07845. 2022."}],"container-title":["Computers, Materials & Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-83-3\/TSP_CMC_61882\/TSP_CMC_61882.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T01:28:26Z","timestamp":1763342906000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v83n3\/61000"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":33,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2025.061882","relation":{},"ISSN":["1546-2226"],"issn-type":[{"type":"electronic","value":"1546-2226"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"2024-12-05","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-02-24","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-05-19","order":2,"name":"published","label":"Published Online","group":{"name":"publication_history","label":"Publication History"}}]}}