Recent large vision models (e.g., SAM) enjoy great potential to facilitate
intelligent perception with high accuracy. Yet, the resource constraints in the
IoT environment tend to limit such large vision models to be locally deployed,
incurring considerable inference latency thereby making it difficult to support
real-time applications, such as autonomous driving and robotics. Edge-cloud
collaboration with large-small model co-inference offers a promising approach
to achieving high inference accuracy and low latency. However, existing
edge-cloud collaboration methods are tightly coupled with the model
architecture and cannot adapt to the dynamic data drifts in heterogeneous IoT
environments. To address the issues, we propose LAECIPS, a new edge-cloud
collaboration framework. In LAECIPS, both the large vision model on the cloud
and the lightweight model on the edge are plug-and-play. We design an
edge-cloud collaboration strategy based on hard input mining, optimized for
both high accuracy and low latency. We propose to update the edge model and its
collaboration strategy with the cloud under the supervision of the large vision
model, so as to adapt to the dynamic IoT data streams. Theoretical analysis of
LAECIPS proves its feasibility. Experiments conducted in a robotic semantic
segmentation system using real-world datasets show that LAECIPS outperforms its
state-of-the-art competitors in accuracy, latency, and communication overhead
while having better adaptability to dynamic environments.

基于 LAECIPS 的云边协作方法在 IoT 环境中通过大型云模型和轻量级边缘模型的协同推理实现了高准确性和低延迟，并适应了动态数据漂移的需求。

LAECIPS: 基于大型视觉模型辅助的适应性边缘云协作技术在物联网感知系统中的应用

LAECIPS: Large Vision Model Assisted Adaptive Edge-Cloud Collaboration  for IoT-based Perception System

Robotic arms are widely used in automatic industries. However, with wide
applications of deep learning in robotic arms, there are new challenges such as
the allocation of grasping computing power and the growing demand for security.
In this work, we propose a robotic arm grasping approach based on deep learning
and edge-cloud collaboration. This approach realizes the arbitrary grasp
planning of the robot arm and considers the grasp efficiency and information
security. In addition, the encoder and decoder trained by GAN enable the images
to be encrypted while compressing, which ensures the security of privacy. The
model achieves 92% accuracy on the OCID dataset, the image compression ratio
reaches 0.03%, and the structural difference value is higher than 0.91.

本文基于深度学习和边缘云协作提出了机械臂抓取方法，并考虑了抓取效率和信息安全。使用 GAN 训练的编码器和解码器实现了图像加密压缩，同时保护了隐私安全，在 OCID 数据集上实现了 92% 的准确率、0.03% 的图像压缩比和 0.91 的结构差异度。