Fine-grained visual classification (FGVC) is valuable yet challenging. The difficulty of FGVC
mainly lies in its intrinsic inter-class similarity, intra-class variation, and limited training data …

Fine-grained visual classification (FGVC) is much more challenging than traditional
classification tasks due to the inherently subtle intra-class object variations. Recent works …

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 …

Fine-grained visual classification aims to recognize images belonging to multiple sub-
categories within a same category. It is a challenging task due to the inherently subtle …

Fine-grained visual categorization (FGVC) relies on hierarchical features extracted by deep
convolutional neural networks (CNNs) to recognize closely alike objects. Particularly …

Fine-grained visual categorization (FGVC) is an important but challenging task due to high
intra-class variances and low inter-class variances caused by deformation, occlusion …

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

Fine-grained visual classification (FGVC) is challenging to capture subtle yet distinct visual
cues due to large intra-class and small inter-class variances. To this end, we propose a new …

Fine-grained visual classification (FGVC) is challenging task duo to the subtle discriminative
features. Recently, RA-CNN selects a single feature region of the image, and recursively …

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 …