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• W3204206962 abstract "Fine-grained visual categorization (FGVC) that aims to recognize objects from subcategories with very subtle differences remains a challenging task due to the large intra-class and small inter-class variation caused by, e.g., deformation, occlusion, illumination, background clutter, etc. A great deal of recent work tackles this problem by forcing the network to focus on partial discriminable features using attention mechanisms or part-based methods. However, these methods neglect the point that the network may learn to discriminate objects from identity-unrelated features, for instance, when backgrounds are discriminable in training samples, degrading the network’s generalization ability. In this paper, for the first time, we use disentangled representation learning to disentangle the fine-grained visual feature into two parts: the identity-related feature and the identity-unrelated feature. Only the identity-related feature is used for the final classification. Since identity-unrelated information is neglected in classification, intra-class variation is reduced while inter-class variation is amplified through the disentanglement, improving the classification performance as a result. Experimental results on three standard fine-grained visual categorization datasets, i.e., CUB-200-2011 (CUB), Stanford Cars (CAR) and FGVC-Aircraft (AIR), demonstrate the effectiveness of our method and show that we achieve state-of-the-art performance on the benchmarks." @default.
• W3204206962 created "2021-10-11" @default.
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• W3204206962 date "2021-01-01" @default.
• W3204206962 modified "2023-09-27" @default.
• W3204206962 title "Learning Disentangled Representation for Fine-Grained Visual Categorization" @default.
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• W3204206962 doi "https://doi.org/10.1007/978-3-030-87355-4_28" @default.
• W3204206962 hasPublicationYear "2021" @default.
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