Bilinear models has been shown to achieve impressive performance on a wide range of
visual tasks, such as semantic segmentation, fine grained recognition and face recognition …

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 …

We propose a simple and straightforward way of creating powerful image representations
via cross-dimensional weighting and aggregation of deep convolutional neural network …

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 …

Recognizing fine-grained sub-categories such as birds and dogs is extremely challenging
due to the highly localized and subtle differences in some specific parts. Most previous …

Most convolutional neural networks (CNNs) lack midlevel layers that model semantic parts
of objects. This limits CNN-based methods from reaching their full potential in detecting and …

Fine-grained recognition is challenging due to its subtle local inter-class differences versus
large intra-class variations such as poses. A key to address this problem is to localize …

Existing fine-grained visual categorization methods often suffer from three challenges: lack
of training data, large number of fine-grained categories, and high intra-class vs. low inter …

We are dealing with the problem of fine-grained vehicle make&model recognition and
verification. Our contribution is showing that extracting additional data from the video stream …

Skin disease is one of the most common human illnesses. It pervades all cultures, occurs at
all ages, and affects between 30% and 70% of individuals, with even higher rates in at-risk …