  Fine-Grained Visual Categorization (FGVC) is a challenging topic in computer
vision. It is a problem characterized by large intra-class differences and
subtle inter-class differences. In this paper, we tackle this problem in a
weakly supervised manner, where neural network models are getting fed with
additional data using a data augmentation technique through a visual attention
mechanism. We perform domain adaptive knowledge transfer via fine-tuning on our
base network model. We perform our experiment on six challenging and commonly
used FGVC datasets, and we show competitive improvement on accuracies by using
attention-aware data augmentation techniques with features derived from deep
learning model InceptionV3, pre-trained on large scale datasets. Our method
outperforms competitor methods on multiple FGVC datasets and showed competitive
results on other datasets. Experimental studies show that transfer learning
from large scale datasets can be utilized effectively with visual attention
based data augmentation, which can obtain state-of-the-art results on several
FGVC datasets. We present a comprehensive analysis of our experiments. Our
method achieves state-of-the-art results in multiple fine-grained
classification datasets including challenging CUB200-2011 bird, Flowers-102,
and FGVC-Aircrafts datasets.
