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J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2024,12,15]]},"abstract":"<jats:p> Most fine-grained visual recognition methods endeavor to directly locate discriminative regions in intricate environments, but tend to overlook the object\u2019s holistic structure, which may lead to misclassification due to overemphasizing incorrect areas. In this paper, we propose a coarse-to-fine paradigm, which prioritizes locating holistic structural regions of the target object, followed by a gradual search to locate discriminative areas. Specifically, we first design the \u201clook into object\u201d module to locate the areas encompassing the target\u2019s holistic structure using prior information. Subsequently, without introducing additional parameters, we design a partial focus searching module to enhance feature representations of discriminative regions within the target\u2019s structural composition. Ultimately, we segregate the foreground components from the original image, attaining a more precise characterization of the target. Furthermore, we demonstrate the practical application potential of our model in real-world industries through our self-constructed DHU-Fine-grained-6000 dataset. Comparative experiments on three public datasets indicate that the superiority of our approach over many recent methods and holds promising application potential in industrial production processes. <\/jats:p>","DOI":"10.1142\/s0218001424350019","type":"journal-article","created":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T09:56:27Z","timestamp":1726221387000},"source":"Crossref","is-referenced-by-count":0,"title":["CTF-Net: Partial Focus Searching within Holistic Structure for Fine-Grained Object Recognition"],"prefix":"10.1142","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6802-2699","authenticated-orcid":false,"given":"Jintao","family":"Li","sequence":"first","affiliation":[{"name":"Engineering Research Center of Digitized Textile Apparel Technology, Ministry of Education, College of Information Sciences and Technology, Donghua University, Shanghai 201620, P. R. 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