In the last few years, deep learning has led to very good performance on a variety of
problems, such as visual recognition, speech recognition and natural language processing …

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 …

Deep learning (DL) techniques have obtained remarkable achievements on various tasks,
such as image recognition, object detection, and language modeling. However, building a …

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 …

In the context of fine-grained visual categorization, the ability to interpret models as human-
understandable visual manuals is sometimes as important as achieving high classification …

Background and objective SARS-CoV-2, a novel strain of coronavirus' also called
coronavirus disease 19 (COVID-19), a highly contagious pathogenic respiratory viral …

We aim to divide the problem space of fine-grained recognition into some specific regions.
To achieve this, we develop a unified framework based on a mixture of experts. Due to …

Face recognition capabilities have recently made extraordinary leaps. Though this progress
is at least partially due to ballooning training set sizes–huge numbers of face images …

Current approaches for fine-grained recognition do the following: First, recruit experts to
annotate a dataset of images, optionally also collecting more structured data in the form of …

摘要细粒度图像分类问题是计算机视觉领域一项极具挑战的研究课题, 其目标是对子类进行识别
, 如区分不同种类的鸟. 由于子类别间细微的类间差异和较大的类内差异, 传统的分类算法不得不 …