  Learning feature representation from discriminative local regions plays a key
role in fine-grained visual classification. Employing attention mechanisms to
extract part features has become a trend. However, there are two major
limitations in these methods: First, they often focus on the most salient part
while neglecting other inconspicuous but distinguishable parts. Second, they
treat different part features in isolation while neglecting their
relationships. To handle these limitations, we propose to locate multiple
different distinguishable parts and explore their relationships in an explicit
way. In this pursuit, we introduce two lightweight modules that can be easily
plugged into existing convolutional neural networks. On one hand, we introduce
a feature boosting and suppression module that boosts the most salient part of
feature maps to obtain a part-specific representation and suppresses it to
force the following network to mine other potential parts. On the other hand,
we introduce a feature diversification module that learns semantically
complementary information from the correlated part-specific representations.
Our method does not need bounding boxes/part annotations and can be trained
end-to-end. Extensive experimental results show that our method achieves
state-of-the-art performances on several benchmark fine-grained datasets.
