Near real-time edge analytics requires dealing with the rapidly growing amount of data,
limited resources, and high failure probabilities of edge nodes. Therefore, data replication is …

Accurate and real-time instance segmentation on mobile devices enables a wide spectrum
of applications such as augmented reality, context-aware inspection and environ-mental …

Object detection models shipped with camera-equipped edge devices cannot cover the
objects of interest for every user. Therefore, the incremental learning capability is a critical …

Recent developments in Artificial Intelligence (AI) research enable new strategies for
running Machine Learning (ML) models. Evaluating application data on a remote server …

Edge analytics receives an ever-increasing interest since processing streaming data closer
to where they are produced, rather than transferring them to the cloud, ensures lower latency …

Abstract We propose Scale-aware AutoAug to learn data augmentation policies for object
detection. We define a new scale-aware search space, where both image-and box-level …

Deep neural networks have proven increasingly important for automotive scene
understanding with new algorithms offering constant improvements of the detection …

The proliferation of IoT devices has led to various computer vision applications, where
addressing bandwidth and latency challenges through edge nodes presents significant …

Edge computing is being widely used for video analytics. To alleviate the inherent tension
between accuracy and cost, various video analytics pipelines have been proposed to …

The increasing use of Internet of Things devices coincides with more communication and
data movement in networks, which can exceed existing network capabilities. These devices …