In the Internet-of-Things (IoT) era, billions of sensors and devices collect and process data
from the environment, transmit them to cloud centers, and receive feedback via the Internet …

Deep learning has developed as an effective machine learning method that takes in
numerous layers of features or representation of the data and provides state-of-the-art …

Recent advances in machine learning and prevalence of digital medical images have
opened up an opportunity to address the challenging brain tumor segmentation (BTS) task …

Deep learning, as one of the most currently remarkable machine learning techniques, has
achieved great success in many applications such as image analysis, speech recognition …

The success of deep learning has been a catalyst to solving increasingly complex machine-
learning problems, which often involve multiple data modalities. We review recent advances …

Magnetic resonance imaging (MRI) is a widely used neuroimaging technique that can
provide images of different contrasts (ie, modalities). Fusing this multi-modal data has …

In this paper, we consider the problem of pedestrian detection in natural scenes. Intuitively,
instances of pedestrians with different spatial scales may exhibit dramatically different …

In order to ensure stable grip** performance of manipulator in a dynamic environment, a
target object grab setting model based on the candidate region suggestion network is …

We introduce the KAIST multi-spectral data set, which covers a great range of drivable
regions, from urban to residential, for autonomous systems. Our data set provides the …

Recently, convolutional neural network (CNN) visual features have demonstrated their
powerful ability as a universal representation for various recognition tasks. In this paper …