AxiomVision: Accuracy-Guaranteed Adaptive Visual Model Selection for Perspective-Aware Video Analytics
<|reference_start|>AxiomVision: Accuracy-Guaranteed Adaptive Visual Model Selection for Perspective-Aware Video Analytics: 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.<|reference_end|>
@article{dai2024axiomvision:,
    title={AxiomVision: Accuracy-Guaranteed Adaptive Visual Model Selection for
  Perspective-Aware Video Analytics},
    author={Xiangxiang Dai, Zeyu Zhang, Peng Yang, Yuedong Xu, Xutong Liu, John
  C.S. Lui},
    journal={arXiv preprint arXiv:2407.20124},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.20124},
    primaryClass={cs.MM cs.AI}
}