Abstract. Attention-based learning for fine-grained image recognition
remains a challenging task, where most of the existing methods treat each
object part in isolation, while neglecting the correlations among them. In
addition, the multi-stage or multi-scale mechanisms involved make the
existing methods less efficient and hard to be trained end-to-end. In this
paper, we propose a novel attention-based convolutional neural network
(CNN) which regulates multiple object parts among different input images. Our method first learns multiple attention region features of each
input image through the one-squeeze multi-excitation (OSME) module,
and then apply the multi-attention multi-class constraint (MAMC) in a
metric learning framework. For each anchor feature, the MAMC functions by pulling same-attention same-class features closer, while pushing different-attention or different-class features away. Our method can
be easily trained end-to-end, and is highly efficient which requires only
one training stage. Moreover, we introduce Dogs-in-the-Wild, a comprehensive dog species dataset that surpasses similar existing datasets by
category coverage, data volume and annotation quality. Extensive experiments are conducted to show the substantial improvements of our
method on four benchmark datasets