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In this paper, we address the extraction of the fine-grained attributes of aninstance as a `multi-attribute classification' problem. To this end, we proposean end-to-end architecture by adopting the bi-linear Convolutional NeuralNetwork with the pairwise ranking loss. This is the first time sucharchitecture is applied for the fine-grained attributes classification problem.We compared the proposed method with a competitive deep Convolutional NeuralNetwork baseline. Extensive experiments show that the proposed methodattains/outperforms the performance of compared baseline with significantlyless number of parameters ($40\times$ less). We demonstrated our approach onCUB200 birds dataset whose annotations are adapted in this work formulti-attribute classification at fine-grained level.

FineTag: Multi-attribute Classification at Fine-grained Level in Images