Internet-enabled smartphones and ultra-wide displays are transforming a variety of visual
apps spanning from on-demand movies and 360° videos to video-conferencing and live …

Thanks to the recent advances in processing speed, data acquisition and storage, machine
learning (ML) is penetrating every facet of our lives, and transforming research in many …

The predominant paradigm for using machine learning models on a device is to train a
model in the cloud and perform inference using the trained model on the device. However …

In recent years, convolutional networks have demonstrated unprecedented performance in
the image restoration task of super-resolution (SR). SR entails the upscaling of a single low …

Deep neural networks (DNNs) continue to demonstrate superior generalization performance
in an increasing range of applications, including speech recognition and image …

Object detection techniques that achieve state-of-the-art detection accuracy employ
convolutional neural networks, implemented to have lower latency in graphics processing …

With an unprecedented accuracy in numerous AI tasks, convolutional neural networks
(CNNs) are rapidly deployed on power-limited mobile and embedded applications. Existing …

Safe driving and reducing the number of accidents victims have been the main motivations
for researchers and automotive companies for decades. Today, humanity is very close to …

Cancer detection from histopathology images is based on extraction of spatial information
from whole slide images (WSI) using image processing tools. Machine learning algorithms …

Object detection techniques that achieve state-of-the-art detection accuracy employ
convolutional neural networks, implemented to have lower latency in graphics processing …