April 2, 2024, 7:49 p.m. | Jiawei Shao, Xinjie Zhang, Jun Zhang

cs.CV updates on arXiv.org arxiv.org

arXiv:2211.14049v3 Announce Type: replace-cross
Abstract: With the development of artificial intelligence (AI) techniques and the increasing popularity of camera-equipped devices, many edge video analytics applications are emerging, calling for the deployment of computation-intensive AI models at the network edge. Edge inference is a promising solution to move the computation-intensive workloads from low-end devices to a powerful edge server for video analytics, but the device-server communications will remain a bottleneck due to the limited bandwidth. This paper proposes a task-oriented communication …

abstract ai models analytics applications artificial artificial intelligence arxiv communication computation cs.cv deployment development devices edge eess.sp inference intelligence low network network edge solution type video video analytics workloads

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