AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection.

Area of Science:

• Computer Vision
• Machine Learning
• Artificial Intelligence

Background:

• Unsupervised domain adaptation for object detection is crucial for real-world applications but underexplored.
• Existing methods struggle with limited annotated data and insufficient feature alignment for domain-invariant representations.

Purpose of the Study:

• To propose a novel Augmented Feature Alignment Network (AFAN) to address limitations in unsupervised domain adaptation for object detection.
• To enhance feature alignment and learn domain-invariant representations effectively.

Main Methods:

• Integration of intermediate domain image generation and domain-adversarial training within a unified framework.
• Utilizing an intermediate domain image generator with soft domain labels to bridge domain divergence.
• Implementing feature pyramid alignment and region feature alignment with corresponding discriminators for multi-scale and instance-level feature alignment.

Main Results:

• The proposed AFAN significantly outperforms state-of-the-art methods on standard benchmarks for both similar and dissimilar domain adaptations.
• Extensive experiments validate the effectiveness of individual components of the AFAN.
• The network demonstrates a strong capability in learning domain-invariant representations.

Conclusions:

• The novel AFAN effectively tackles challenges in unsupervised domain adaptation for object detection.
• The integrated approach of intermediate domain image generation and multi-level feature alignment is key to its success.
• AFAN provides a robust solution for learning domain-invariant features, advancing the field of object detection.