DOMAIN-INVARIANT REGION PROPOSAL NETWORK FOR CROSS-DOMAIN DETECTION

The performances of object detectors are highly impacted by the discrepancy between existing data sets and application scenarios, leading to the so-called domain shift problem. Previous works, based on Faster R-CNN, focus on aligning the image-level features and the region-level features. However, the Region Proposal Network (RPN), as a key module between the image-level and the region-level modules, still has the problem of domain shift that leads to inaccurate or even false detected results. To tackle this issue, we propose a new design, Domain-Invariant RPN (DIR), which adopts adversarial learning to eliminate the domain shift in RPN, and thereby, significantly improving the accuracy and robustness of bounding box proposals. Furthermore, we propose a Double-Consistency Regularization (DCR) to improve the overall feature alignment. Extensive experiments show that our approach outperforms state-of-the-art methods.