Intermediate Domain-Based Meta Learning Framework for Adaptive Object Detection

Abstract

Deep learning based object detection methods have made significant progress in recent years. However, these methods often suffer from a substantial performance drop when domain shifts occur, making it difficult to generalize a source domain trained object detector to a new target domain. To address this problem, we propose an Online Meta Learning Framework (OMLF) for unsupervised domain adaptive object detection. In our proposed framework, we adopt the Polar Harmonic Fourier Moment (PHFM) to generate target-like intermediate data. The purpose is to construct a two-pair framework that learns meta knowledge (i.e. model initial parameters) from the pair of "source-to-intermediate" to assist another pair of "intermediate-to-target". Moreover, the optimizing process requires a heavy computational load due to triggering higher-order gradients. To alleviate this problem, we introduce a shortest-path update strategy that accelerates optimization. When evaluated on several benchmark adaptation scenarios (i.e. normal-to-foggy weather, cross cameras, synthetic-to-real, and real-to-artistic), our OMLF achieves state-of-the-art results, demonstrating its effectiveness.