  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.
