Extracting and using class-discriminative features is critical for fine-grained recognition.
Existing works have demonstrated the possibility of applying deep CNNs to exploit features …

We aim to divide the problem space of fine-grained recognition into some specific regions.
To achieve this, we develop a unified framework based on a mixture of experts. Due to …

Extracting and fusing part features have become the key of fined-grained image recognition.
Recently, Non-local (NL) module has shown excellent improvement in image recognition …

Compared to earlier multistage frameworks using CNN features, recent end-to-end deep
approaches for fine-grained recognition essentially enhance the mid-level learning …

Learning mid-level representation for fine-grained recognition is easily dominated by a
limited number of highly discriminative patterns, degrading its robustness and generalization …

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 …

Fine-grained recognition distinguishes among categories with subtle visual differences. In
order to differentiate between these challenging visual categories, it is helpful to leverage …

Fine-grained visual classification (FGVC) which aims at recognizing objects from
subcategories is a very challenging task due to the inherently subtle inter-class differences …

Fine-grained recognition poses the challenge of discriminating categories with only small
subtle visual differences, which can be easily overwhelmed by diverse appearance within …

Recently, with the emergence of retrieval requirements for certain individual in the same
superclass, eg, birds, persons, cars, fine-grained recognition task has attracted a significant …