Multi-Stream Cellular Test-Time Adaptation of Real-Time Models Evolving in Dynamic Environments

Benoît Gérin, Anaïs Halin, Anthony Cioppa, Maxim Henry, Bernard Ghanem, Benoît Macq, Christophe De Vleeschouwer, Marc Van Droogenbroeck; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 4472-4482

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.