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.

本文提出了一种深度双线性变换（DBT）块，可以在卷积神经网络中深度堆叠，以学习细粒度图像表示，其利用双线性变换在每个语义组内计算成对交互的方式，显著减轻了计算成本，能够在 CUB-Bird、Stanford-Car 和 FGVC-Aircraft 等多个细粒度图像识别基准测试中取得新的最佳表现。