  Fine-grained visual categorization (FGVC) aims to discriminate similar
subcategories, whose main challenge is the large intraclass diversities and
subtle inter-class differences. Existing FGVC methods usually select
discriminant regions found by a trained model, which is prone to neglect other
potential discriminant information. On the other hand, the massive interactions
between the sequence of image patches in ViT make the resulting class-token
contain lots of redundant information, which may also impacts FGVC performance.
In this paper, we present a novel approach for FGVC, which can simultaneously
make use of partial yet sufficient discriminative information in environmental
cues and also compress the redundant information in class-token with respect to
the target. Specifically, our model calculates the ratio of high-weight regions
in a batch, adaptively adjusts the masking threshold and achieves moderate
extraction of background information in the input space. Moreover, we also use
the Information Bottleneck~(IB) approach to guide our network to learn a
minimum sufficient representations in the feature space. Experimental results
on three widely-used benchmark datasets verify that our approach can achieve
outperforming performance than other state-of-the-art approaches and baseline
models.
