  Deep Neural Network has shown great strides in the coarse-grained image
classification task. It was in part due to its strong ability to extract
discriminative feature representations from the images. However, the marginal
visual difference between different classes in fine-grained images makes this
very task harder. In this paper, we tried to focus on these marginal
differences to extract more representative features. Similar to human vision,
our network repetitively focuses on parts of images to spot small
discriminative parts among the classes. Moreover, we show through
interpretability techniques how our network focus changes from coarse to fine
details. Through our experiments, we also show that a simple attention model
can aggregate (weighted) these finer details to focus on the most dominant
discriminative part of the image. Our network uses only image-level labels and
does not need bounding box/part annotation information. Further, the simplicity
of our network makes it an easy plug-n-play module. Apart from providing
interpretability, our network boosts the performance (up to 2%) when compared
to its baseline counterparts. Our codebase is available at
https://github.com/TAMU-VITA/Focus-Longer-to-See-Better
