Research on Long-Tail Visual Recognition Technology with Edge-Side Domain Adaptation

The deployment of deep learning models at the edge is hindered by challenges such as domain offset, long-tail distribution, and limited computing resources in the training data. Therefore, domain adaptation methods must be applied for online retraining to alleviate the domain offset, and long-tail reduction techniques must be applied during retraining to alleviate the long-tail problem while considering computational costs. However, most existing long-tail reduction techniques have high computational costs or cannot be effectively combined with domain adaptation methods. To address these issued, this paper proposes EdgeTailor, a long-tail optimization method specifically designed for edge-side domain adaptation. EdgeTailor optimizes the continuous unsupervised adaptive process by using synthetic minority class oversampling techniques and class-balanced loss as strategies for tail truncation. Consequently, a buffer is introduced to address the issue of insufficient data for tail classes during online learning, allowing it to mitigate the long-tail problem while conducting online continuous domain adaptation. Experimental results demonstrate the effectiveness of EdgeTailor in edge domain adaptation tasks involving two long-tail datasets with domain shift. Using five deep neural networks as the model backbone, EdgeTailor improves average Top-1 accuracy by approximately 8.10% compared with the baseline in the target domain. In terms of computational cost, EdgeTailor maintains a low level of Floating Point Operations Per Second (FLOPs) and parameter count, reducing FLOPs by approximately 29.84% compared with the data synthesis method, with better performance than the baseline. Overall, EdgeTailor achieves high performance and low cost in addressing both domain adaptation and long-tail visual recognition challenges in edge deployment.