Object categories inherently form a hierarchy with different levels of concept abstraction,
especially for fine-grained categories. For example, birds (Aves) can be categorized …

The underlining task for fine-grained image recognition captures both the inter-class and
intra-class discriminate features. Existing methods generally use auxiliary data to guide the …

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 …

Data augmentation is usually adopted to increase the amount of training data, prevent
overfitting and improve the performance of deep models. However, in practice, random data …

Fine-grained visual classification aims to recognize images belonging to multiple sub-
categories within a same category. It is a challenging task due to the inherently subtle …

This work addresses fine-grained image classification. Our work is based on the hypothesis
that when dealing with subtle differences among object classes it is critical to identify and …

Fine-grained recognition poses the unique challenge of capturing subtle inter-class
differences under considerable intra-class variances (eg, beaks for bird species) …

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

Fine-grained recognition concerns categorization at sub-ordinate levels, where the
distinction between object classes is highly local. Compared to basic level recognition, fine …

How to extract distinctive features greatly challenges the fine-grained image classification
tasks. In previous models, bilinear pooling has been frequently adopted to address this …