EdgeTail: Mitigating Long-Tail Visual Problems in Continual Learning at Edge

Large vision and language models deployed at edge encounter continuously evolving input distributions, including not only new tasks but also highly unbalanced long-tail classes. For example, smart-surveillance cameras frequently capture common objects such as pedestrians and cars, while only occasionally observing tail classes like horseback riders or stroller pushers. However, most existing long-tail mitigation techniques are designed for fixed pretraining data. This often leads to poor accuracy of tail classes on evolving data and incurs high computational costs on edge devices. In this article, we propose EdgeTail, a lightweight long-tail mitigation method for edge-side continual learning. EdgeTail’s key design features are: (i) optimal long-tailed mitigation solution search, which adaptively selects the best long-tail learning method for the current distribution/task; and (ii) graph attention classifier and multi-branch adapter, which improves the quality and stability of tail class representations with small overheads. We implement EdgeTail in PyTorch and extensively evaluate it against state-of-the-art methods. The results show that EdgeTail improves the average accuracy by 36.09% under fixed training windows and by 31.12% under different training window sizes.