Deep learning shows great promise in providing more intelligence to augmented reality (AR)
devices, but few AR apps use deep learning due to lack of infrastructure support. Deep …

Deep learning has the potential to make Augmented Reality (AR) devices smarter, but few
AR apps use such technology today because it is compute-intensive, and front-end devices …

Many mobile applications have been developed to apply deep learning for video analytics.
Although these advanced deep learning models can provide us with better results, they also …

Real-time on-device object detection for video analytics fails to meet the accuracy
requirement due to limited resources of mobile devices while offloading object detection …

Mobile vision systems powered by the recent advancement in Deep Neural Networks
(DNNs) are enabling a wide range of on-device video analytics applications. Considering …

Modern mobile applications are benefiting significantly from the advancement in deep
learning, eg, implementing real-time image recognition and conversational system. Given a …

As mobile devices continuously generate streams of images and videos, a new class of
mobile deep vision applications are rapidly emerging, which usually involve running deep …

Widespread adoption of mobile augmented reality (AR) and virtual reality (VR) applications
depends on their smoothness and immersiveness. Modern AR applications applying …

The advancement in deep learning and edge computing has enabled intelligent mobile
augmented reality (MAR) on resource limited mobile devices. However, today very few deep …

Video analytics constitute a core component of many wireless services that require
processing of voluminous data streams emanating from handheld devices. Multi-Access …