Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer
vision and pattern recognition, and underpins a diverse set of real-world applications. The …

Compared to traditional machine learning, deep learning can learn deeper abstract data
representation and understand scattered data properties. It has gained considerable …

While GANs have shown success in realistic image generation, the idea of using GANs for
other tasks unrelated to synthesis is underexplored. Do GANs learn meaningful structural …

The need of collecting large quantities of labeled training data for each new task has limited
the usefulness of deep neural networks. Given data from a set of source tasks, this limitation …

Data augmentation is an intuitive step towards solving the problem of few-shot classification.
However, ensuring both discriminability and diversity in the augmented samples is …

Humans have the ability to robustly recognize objects with various factors of variations such
as nonrigid transformations, background noises, and changes in lighting conditions …

The goal of few-shot fine-grained image classification (FSFGIC) is to distinguish subordinate-
level categories with subtle visual differences such as the species of bird and models of car …

State-of-the-art deep learning systems (eg, ImageNet image classification) typically require
very large training sets to achieve high accuracies. Therefore, one of the grand challenges is …

In this paper, we study the fine-grained categorization problem under the few-shot setting, ie,
each fine-grained class only contains a few labeled examples, termed Fine-Grained Few …

Few-shot image classification aims at exploring transferable features from base classes to
recognize images of the unseen novel classes with only a few labeled images. Existing …