Multi-level Domain Adaptive learning for Cross-Domain Detection
This work addresses cross-domain detection challenges for computer vision applications, representing an incremental improvement over existing adversarial training methods.
The paper tackles the problem of cross-domain object detection by proposing a multi-level domain adaptive model that aligns both local and global feature distributions, achieving superior performance against state-of-the-art techniques in most object categories.
In recent years, object detection has shown impressive results using supervised deep learning, but it remains challenging in a cross-domain environment. The variations of illumination, style, scale, and appearance in different domains can seriously affect the performance of detection models. Previous works use adversarial training to align global features across the domain shift and to achieve image information transfer. However, such methods do not effectively match the distribution of local features, resulting in limited improvement in cross-domain object detection. To solve this problem, we propose a multi-level domain adaptive model to simultaneously align the distributions of local-level features and global-level features. We evaluate our method with multiple experiments, including adverse weather adaptation, synthetic data adaptation, and cross camera adaptation. In most object categories, the proposed method achieves superior performance against state-of-the-art techniques, which demonstrates the effectiveness and robustness of our method.