@inproceedings{
dai2024axiomvision,
title={AxiomVision: Accuracy-Guaranteed Adaptive Visual Model Selection for Perspective-Aware Video Analytics},
author={Xiangxiang DAI and Zeyu Zhang and Peng Yang and Yuedong Xu and Xutong Liu and John C.S. Lui},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=Oq8AYY6qTY}
}
The rapid evolution of multimedia and computer vision technologies requires adaptive visual model deployment strategies to effectively handle diverse tasks and varying environments. This work introduces  \textit{AxiomVision}, a novel framework that can guarantee accuracy by leveraging edge computing to dynamically select the most efficient visual models for video analytics under diverse scenarios. Utilizing a tiered edge-cloud architecture, \textit{AxiomVision} enables the deployment of a broad spectrum of visual models, from lightweight to complex DNNs, that can be tailored to specific scenarios while considering camera source impacts. In addition,  \textit{AxiomVision}  provides three core innovations: (1) a dynamic visual model selection mechanism utilizing continual online learning, (2) an efficient online method that efficiently takes into account the influence of the camera's perspective, and (3) a topology-driven grouping approach that accelerates the model selection process. With rigorous theoretical guarantees, these advancements provide a scalable and effective solution for visual tasks inherent to multimedia systems, such as object detection,  classification, and counting. Empirically, \textit{AxiomVision} achieves a 25.7\% improvement in accuracy.
AxiomVision: Accuracy-Guaranteed Adaptive Visual Model Selection for Perspective-Aware Video Analytics
Xiangxiang DAI Zeyu Zhang Peng Yang Yuedong Xu Xutong Liu John C.S. Lui