  Bilinear feature transformation has shown the state-of-the-art performance in
learning fine-grained image representations. However, the computational cost to
learn pairwise interactions between deep feature channels is prohibitively
expensive, which restricts this powerful transformation to be used in deep
neural networks. In this paper, we propose a deep bilinear transformation (DBT)
block, which can be deeply stacked in convolutional neural networks to learn
fine-grained image representations. The DBT block can uniformly divide input
channels into several semantic groups. As bilinear transformation can be
represented by calculating pairwise interactions within each group, the
computational cost can be heavily relieved. The output of each block is further
obtained by aggregating intra-group bilinear features, with residuals from the
entire input features. We found that the proposed network achieves new
state-of-the-art in several fine-grained image recognition benchmarks,
including CUB-Bird, Stanford-Car, and FGVC-Aircraft.
