AxiomVision：面向视角感知视频分析的精度保证自适应视觉模型选择
AxiomVision: Accuracy-Guaranteed Adaptive Visual Model Selection for Perspective-Aware Video Analytics
AI总结：
提出AxiomVision框架，利用边缘计算和分层边缘云架构，通过持续在线学习、相机视角感知和拓扑驱动分组动态选择视觉模型，在保证理论精度的同时实现25.7%的精度提升。
摘要：
多媒体与计算机视觉技术的快速发展要求自适应的视觉模型部署策略，以有效处理多样化任务与多变环境。本工作提出AxiomVision，一个利用边缘计算在不同场景下为视频分析动态选择最高效视觉模型从而保证精度的全新框架。采用分层边缘云架构，AxiomVision支持部署从轻量级到复杂DNN的广泛视觉模型，并能在考虑相机源影响的同时针对特定场景进行定制。此外，AxiomVision提供三项核心创新：（1）利用持续在线学习的动态视觉模型选择机制；（2）一种高效考虑相机视角影响的在线方法；（3）一种加速模型选择过程的拓扑驱动分组方法。凭借严格的理论保证，这些进展为多媒体系统固有的视觉任务（如目标检测、分类和计数）提供了可扩展且有效的解决方案。实验表明，AxiomVision实现了25.7%的精度提升。
英文摘要：
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 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, 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, 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, AxiomVision achieves a 25.7\% improvement in accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
