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 …

Taking time to identify expected products and waiting for the checkout in a retail store are
common scenes we all encounter in our daily lives. The realization of automatic product …

Attention-based learning for fine-grained image recognition remains a challenging task,
where most of the existing methods treat each object part in isolation, while neglecting the …

Fine-grained image classification is to recognize hundreds of subcategories belonging to
the same basic-level category, such as 200 subcategories belonging to the bird, which is …

This paper introduces WILDCAT, a deep learning method which jointly aims at aligning
image regions for gaining spatial invariance and learning strongly localized features. Our …

Scaling up fine-grained recognition to all domains of fine-grained objects is a challenge the
computer vision community will need to face in order to realize its goal of recognizing all …

Fine-grained object classification attracts increasing attention in multimedia applications.
However, it is a quite challenging problem due to the subtle interclass difference and large …

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 …

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 …

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 …