Cross-Domain Object Detection by Dual Adaptive Branch.

Area of Science:

• Computer Vision
• Machine Learning
• Artificial Intelligence

Background:

• Object detection typically assumes identical training and testing data distributions, which is often not true in real-world scenarios.
• Cross-domain object detection is crucial for adapting models to new environments but presents challenges in both classification and localization.
• Existing teacher-student frameworks improve performance using pseudo-labels, often employing feature-level adversarial training for domain alignment.

Purpose of the Study:

• To propose a novel teacher-student framework, Dual Adaptive Branch (DAB), for enhanced cross-domain object detection.
• To address the challenge of domain distribution shift by aligning domain-invariant features and suppressing domain-specific features.
• To improve both classification and localization accuracy in target domains with different data distributions.

Main Methods:

• Introduced a Dual Adaptive Branch (DAB) teacher-student framework.
• Employed domain adversarial learning to manage domain distribution differences.
• Focused on aligning domain-invariant features while mitigating the influence of domain-specific features within the student model.

Main Results:

• The DAB framework significantly enhanced the performance of cross-domain object detection.
• Experimental validation across multiple domains demonstrated the method's effectiveness.
• Achieved competitive results on standard benchmarks for cross-domain object detection tasks.

Conclusions:

• The proposed Dual Adaptive Branch (DAB) framework effectively tackles domain shift in object detection.
• DAB's approach of aligning invariant features and suppressing specific ones leads to superior performance.
• The method shows strong potential for real-world applications requiring robust object detection across diverse domains.