Convolutional neural networks (CNNs) have been applied to visual tasks since the late
1980s. However, despite a few scattered applications, they were dormant until the mid …

The field of computer vision is experiencing a great-leap-forward development today. This
paper aims at providing a comprehensive survey of the recent progress on computer vision …

Surgical workflow recognition has numerous potential medical applications, such as the
automatic indexing of surgical video databases and the optimization of real-time operating …

Abstract The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object
category classification and detection on hundreds of object categories and millions of …

In the past years, the computer vision domain has been profoundly changed by the advent of
deep learning algorithms and data science. The defect detection problem is of outmost …

Nearest neighbor search is a problem of finding the data points from the database such that
the distances from them to the query point are the smallest. Learning to hash is one of the …

Deep neural nets with a large number of parameters are very powerful machine learning
systems. However, overfitting is a serious problem in such networks. Large networks are …

Attributes act as intermediate representations that enable parameter sharing between
classes, a must when training data is scarce. We propose to view attribute-based image …

Abstract Deep Neural Networks (DNNs) have recently shown outstanding performance on
the task of whole image classification. In this paper we go one step further and address the …

We trained a large, deep convolutional neural network to classify the 1.3 million high-
resolution images in the LSVRC-2010 ImageNet training set into the 1000 different classes …