Semantic patterns of fine-grained objects are determined by subtle appearance difference of
local parts, which thus inspires a number of part-based methods. However, due to …

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 …

Deep convolutional neural networks (CNNs) have shown a strong ability in mining
discriminative object pose and parts information for image recognition. For fine-grained …

Pose variation and subtle differences in appearance are key challenges to fine-grained
classification. While deep networks have markedly improved general recognition, many …

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 object recognition (FGOR) aims to learn discriminative features that can
identify the subtle distinctions between visually similar objects. However, less effort has …

Fine-grained object classification attracts increasing attention in multimedia applications.
However, it is a quite challenging problem due to the subtle interclass difference and large …

Recent works such as Mixup and Cutmix have demonstrated the effectiveness of
augmenting training data for deep models. These methods generate new data by generally …

Fine-grained object classification (FGOC) is a challenging research topic in multimedia
computing with machine learning, which faces two pivotal conundrums: focusing attention …

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 …