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 …

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

Fine-grained image recognition is a longstanding computer vision challenge that focuses on
differentiating objects belonging to multiple subordinate categories within the same meta …

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 …

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 …

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

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 …

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 image recognition puts forward a special challenge due to the difficulties of
distinguishing subtle inter-class differences and large intra-class variances. Existing weakly …

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 …