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- W2898371517 abstract "In this paper, we address the extraction of the fine-grained attributes of an instance as a `multi-attribute classification' problem. To this end, we propose an end-to-end architecture by adopting the bi-linear Convolutional Neural Network with the pairwise ranking loss. This is the first time such architecture is applied for the fine-grained attributes classification problem. We compared the proposed method with a competitive deep Convolutional Neural Network baseline. Extensive experiments show that the proposed method attains/outperforms the performance of compared baseline with significantly less number of parameters ($40times$ less). We demonstrated our approach on CUB200 birds dataset whose annotations are adapted in this work for multi-attribute classification at fine-grained level." @default.
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- W2898371517 date "2018-06-19" @default.
- W2898371517 modified "2023-09-25" @default.
- W2898371517 title "FineTag: Multi-attribute Classification at Fine-grained Level in Images" @default.
- W2898371517 hasPublicationYear "2018" @default.
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