Fine-grained image classification is challenging due to the large intra-class variance and
small inter-class variance, aiming at recognizing hundreds of sub-categories belonging to …

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 …

Attention-based learning for fine-grained image recognition remains a challenging task,
where most of the existing methods treat each object part in isolation, while neglecting the …

Most weakly supervised fine-grained image recognition (WFGIR) approaches predominantly
focus on learning the discriminative details which contain the visual variances and position …

Collecting fine-grained labels usually requires expert-level domain knowledge and is
prohibitive to scale up. In this paper, we propose Attribute Mix, a data augmentation strategy …

Fine-grained visual classification (FGVC) aims to identify objects belonging to multiple sub-
categories of the same super-category. The key to solving fine-grained classification …

Recent algorithms in convolutional neural networks (CNN) considerably advance the fine-
grained image classification, which aims to differentiate the subtle differences among …

Existing deep learning based weakly supervised fine-grained image recognition (WFGIR)
methods usually pick out the discriminative regions from the high-level feature (HLF) maps …

For the classification of fine-grained images, the subtle differences among the subclasses of
the main category must be distinguished. Intuitively, the key to realizing the fine-grained …

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