Fine-Grained

Motivation:

The main requisite for fine-grained recognition task is to focus on subtle discriminative details that make the subordinate classes different from each other.

We note that existing methods implicitly address this requirement and leave it to a data-driven pipeline to figure out what makes a subordinate class different from the others.

This results in two major limitations:

First, the network focuses on the most obvious distinctions between classes and overlooks more subtle inter-class variations.

Second, the chance of misclassifying a given sample in any of the negative classes is considered equal, while in fact, confusions generally occur among only the most similar classes.