  Fine-grained recognition, a pivotal task in visual signal processing, aims to
distinguish between similar subclasses based on discriminative information
present in samples. However, prevailing methods often erroneously focus on
background areas, neglecting the capture of genuinely effective discriminative
information from the subject, thus impeding practical application. To
facilitate research into the impact of background noise on models and enhance
their ability to concentrate on the subject's discriminative features, we
propose an engineered pipeline that leverages the capabilities of SAM and Detic
to create fine-grained datasets with only foreground subjects, devoid of
background. Extensive cross-experiments validate this approach as a
preprocessing step prior to training, enhancing algorithmic performance and
holding potential for further modal expansion of the data.
