Data mixing augmentation has proved effective in training deep models. Recent methods
mix labels mainly according to the mixture proportion of image pixels. Due to the major …

Learning representation carrying rich local information is essential for recognizing fine-
grained objects. Existing methods to this task resort to multi-stage frameworks to capture fine …

The challenge of fine-grained visual recognition often lies in discovering the key
discriminative regions. While such regions can be automatically identified from a large-scale …

Constructing fine-grained image datasets typically requires domain-specific expert
knowledge, which is not always available for crowd-sourcing platform annotators …

Deep convolutional neural networks (CNN) have seen tremendous success in large-scale
generic object recognition. In comparison with generic object recognition, fine-grained …

Fine-grained image categorization is challenging due to the subtle inter-class differences.
We posit that exploiting the rich relationships between channels can help capture such …

Given a training dataset composed of images and corresponding category labels, deep
convolutional neural networks show a strong ability in mining discriminative parts for image …

Fine-grained image recognition is a challenging computer vision problem, due to the small
inter-class variations caused by highly similar subordinate categories, and the large intra …

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 …

Traditional fine-grained image recognition is required to distinguish different subordinate
categories (eg, birds species) based on the visual cues beneath raw images. Due to both …