Recent techniques have achieved remarkable improvements depended on mining subtle
yet distinctive features for fine-grained visual classification (FGVC). While prior works directly …

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 …

Deep neural networks usually depend on substantial labeled data and suffer from poor
generalization to new domains. Domain adaptation can be used to resolve these issues …

Weakly-supervised fine-grained visual categorization (FGVC) aims to achieve subclass
classification within the same large class using only label information. Compared to general …

Referring image segmentation aims to segment the target object from the image according
to the description of language expression. Due to the diversity of language expressions …

Fine-grained visual categorization is a challenging issue owing to high intra-class and low
inter-class variances. Classical approaches rely on pre-trained models or many fine …

Hierarchical Fine-Grained Visual Classification (HFGVC) assigns a label sequence (eg,["
Albatross''," Laysan Albatross'']) with a coarse to fine hierarchy to each object. It remains …

Deep representation learning is a subfield of machine learning that focuses on learning
meaningful and useful representations of data through deep neural networks. However …

Vehicle model recognition is a typical fine-grained classification task that has a wide range
of application prospects in safe cities and constitutes a research hotspot in the field of …

Fine-grained image classification is a challenging task due to the small inter-class variance,
the large intra-class difference, and the small training data. Traditional methods typically rely …