  Low resolution fine-grained classification has widespread applicability for
applications where data is captured at a distance such as surveillance and
mobile photography. While fine-grained classification with high resolution
images has received significant attention, limited attention has been given to
low resolution images. These images suffer from the inherent challenge of
limited information content and the absence of fine details useful for
sub-category classification. This results in low inter-class variations across
samples of visually similar classes. In order to address these challenges, this
research proposes a novel attribute-assisted loss, which utilizes ancillary
information to learn discriminative features for classification. The proposed
loss function enables a model to learn class-specific discriminative features,
while incorporating attribute-level separability. Evaluation is performed on
multiple datasets with different models, for four resolutions varying from
32x32 to 224x224. Different experiments demonstrate the efficacy of the
proposed attributeassisted loss for low resolution fine-grained classification.
