Multi-Stream Cellular Test-Time Adaptation of Real-Time Models Evolving in Dynamic Environments
CoRR(2024)
摘要
In the era of the Internet of Things (IoT), objects connect through a dynamic
network, empowered by technologies like 5G, enabling real-time data sharing.
However, smart objects, notably autonomous vehicles, face challenges in
critical local computations due to limited resources. Lightweight AI models
offer a solution but struggle with diverse data distributions. To address this
limitation, we propose a novel Multi-Stream Cellular Test-Time Adaptation
(MSC-TTA) setup where models adapt on the fly to a dynamic environment divided
into cells. Then, we propose a real-time adaptive student-teacher method that
leverages the multiple streams available in each cell to quickly adapt to
changing data distributions. We validate our methodology in the context of
autonomous vehicles navigating across cells defined based on location and
weather conditions. To facilitate future benchmarking, we release a new
multi-stream large-scale synthetic semantic segmentation dataset, called DADE,
and show that our multi-stream approach outperforms a single-stream baseline.
We believe that our work will open research opportunities in the IoT and 5G
eras, offering solutions for real-time model adaptation.
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