The emerging field of artificial intelligence of things (AIoT, AI+ IoT) is driven by the
widespread use of intelligent infrastructures and the impressive success of deep learning …

In recent years, deep learning (DL) models have demonstrated remarkable achievements
on non-trivial tasks such as speech recognition, image processing, and natural language …

Video analytics applications use edge compute servers for processing videos. Compressed
models that are deployed on the edge servers for inference suffer from data drift where the …

Continuous learning has recently shown promising results for video analytics by adapting a
lightweight" expert" DNN model for each specific video scene to cope with the data drift in …

Edge-cloud collaborative video analytics is transforming the way data is being handled,
processed, and transmitted from the ever-growing number of surveillance cameras around …

While large deep neural networks excel at general video analytics tasks, the significant
demand on computing capacity makes them infeasible for real-time inference on resource …

When facing changing environments in the real world, the lightweight model on client
devices suffer from severe performance drop under distribution shifts. The main limitations of …

Deep learning–based video analytics demands high network bandwidth to ferry the large
volume of data when deployed on the cloud. When incorporated at the edge side, only …

This paper proposes Shoggoth, an efficient edge-cloud collaborative architecture, for
boosting inference performance on real-time video of changing scenes. Shoggoth uses …

In this work, we propose an efficient Video-Language Alignment via Frame-Prompting and
Distilling (VLAP) network. Our VLAP model addresses both efficient frame sampling and …