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 …

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

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 …

Current approaches for fine-grained recognition do the following: First, recruit experts to
annotate a dataset of images, optionally also collecting more structured data in the form of …

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 …

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 …

Fine-grained recognition is challenging due to its subtle local inter-class differences versus
large intra-class variations such as poses. A key to address this problem is to localize …

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 object recognition aims to learn effective features that can identify the subtle
differences between visually similar objects. Most of the existing works tend to amplify …

Fine-grained classification is a challenging problem, due to subtle differences among highly-
confused categories. Most approaches address this difficulty by learning discriminative …