  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 ($40\times$ less). We demonstrated our approach on
CUB200 birds dataset whose annotations are adapted in this work for
multi-attribute classification at fine-grained level.
