  Fine-grained visual classification is a challenging task that recognizes the
sub-classes belonging to the same meta-class. Large inter-class similarity and
intra-class variance is the main challenge of this task. Most exiting methods
try to solve this problem by designing complex model structures to explore more
minute and discriminative regions. In this paper, we argue that mining
multi-regional multi-grained features is precisely the key to this task.
Specifically, we introduce a new loss function, termed top-down spatial
attention loss (TDSA-Loss), which contains a multi-stage channel constrained
module and a top-down spatial attention module. The multi-stage channel
constrained module aims to make the feature channels in different stages
category-aligned. Meanwhile, the top-down spatial attention module uses the
attention map generated by high-level aligned feature channels to make
middle-level aligned feature channels to focus on particular regions. Finally,
we can obtain multiple discriminative regions on high-level feature channels
and obtain multiple more minute regions within these discriminative regions on
middle-level feature channels. In summary, we obtain multi-regional
multi-grained features. Experimental results over four widely used fine-grained
image classification datasets demonstrate the effectiveness of the proposed
method. Ablative studies further show the superiority of two modules in the
proposed method. Codes are available at:
https://github.com/dongliangchang/Top-Down-Spatial-Attention-Loss.
