Existing fine-grained image recognition works have attempted to dig into low-level details for
emphasizing subtle discrepancies among sub-categories. However, a potential limitation of …

Fine-grained image classification is a challenging task due to the large inter-class difference
and small intra-class difference. In this paper, we propose a novel Cascade Attention Model …

Fine-grained image recognition is challenging because discriminative clues are usually
fragmented, whether from a single image or multiple images. Despite their significant …

Recognizing objects from subcategories with very subtle differences remains a challenging
task due to the large intra-class and small inter-class variation. Recent work tackles this …

Discriminative region localization and feature learning are crucial for fine-grained visual
recognition. Existing approaches solve this issue by attention mechanism or part based …

In this paper, we study the fine-grained categorization problem under the few-shot setting, ie,
each fine-grained class only contains a few labeled examples, termed Fine-Grained Few …

Fine-grained recognition is one of the most difficult topics in visual recognition, which aims at
distinguishing confusing categories such as bird species within a genus. The information of …

Fine-Grained Visual Classification (FGVC) is the task that requires recognizing the objects
belonging to multiple subordinate categories of a super-category. Recent state-of-the-art …

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 …

Classifying the sub-categories of an object from the same super-category (eg, bird species
and cars) in fine-grained visual classification (FGVC) highly relies on discriminative feature …