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 …

Video streaming is crucial for AI applications that gather videos from sources to servers for
inference by deep neural nets (DNNs). Unlike traditional video streaming that optimizes …

To cope with the high resource (network and compute) demands of real-time video analytics
pipelines, recent systems have relied on frame filtering. However, filtering has typically been …

Today's trend of building ever larger language models (LLMs), while pushing the
performance of natural language processing, adds significant latency to the inference stage …

Live video accounts for a significant volume of today's Internet video. Despite a large
number of efforts to enhance user quality of experience (QoE) both at the ingest and …

Machine learning (ML) has become the de-facto approach for various scientific domains
such as computer vision and natural language processing. Despite recent breakthroughs …

High-definition live streaming has experienced tremendous growth. However, the video
quality of live video is often limited by the streamer's uplink bandwidth. Recently, neural …

Emerging deep learning-based video analytics tasks demand computation-intensive neural
networks and powerful computing resources on the cloud to achieve high accuracy. Due to …

Video analytics pipelines have steadily shifted to edge deployments to reduce bandwidth
overheads and privacy violations, but in doing so, face an ever-growing resource tension …

The advent of high-accuracy and resource-intensive deep neural networks (DNNs) has
fulled the development of live video analytics, where camera videos need to be streamed …