Significant feature suppression and cross-feature fusion networks for fine-grained visual classification
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Researchers have developed a method for fine-grained visual classification using significant feature suppression and cross-feature fusion networks. This approach is intended to improve the accuracy of visual classification tasks. The details of this method have been outlined in a related publication.