  Fine-Grained Visual Classification (FGVC) is known as a challenging task due
to subtle differences among subordinate categories. Many current FGVC
approaches focus on identifying and locating discriminative regions by using
the attention mechanism, but neglect the presence of unnecessary features that
hinder the understanding of object structure. These unnecessary features,
including 1) ambiguous parts resulting from the visual similarity in object
appearances and 2) noninformative parts (e.g., background noise), can have a
significant adverse impact on classification results. In this paper, we propose
the Semantic Feature Integration network (SFI-Net) to address the above
difficulties. By eliminating unnecessary features and reconstructing the
semantic relations among discriminative features, our SFI-Net has achieved
satisfying performance. The network consists of two modules: 1) the multi-level
feature filter (MFF) module is proposed to remove unnecessary features with
different receptive field, and then concatenate the preserved features on pixel
level for subsequent disposal; 2) the semantic information reconstitution (SIR)
module is presented to further establish semantic relations among
discriminative features obtained from the MFF module. These two modules are
carefully designed to be light-weighted and can be trained end-to-end in a
weakly-supervised way. Extensive experiments on four challenging fine-grained
benchmarks demonstrate that our proposed SFI-Net achieves the state-of-the-arts
performance. Especially, the classification accuracy of our model on
CUB-200-2011 and Stanford Dogs reaches 92.64% and 93.03%, respectively.
