Brain-Inspired Domain-Incremental Adaptive Detection for Autonomous Driving.

Area of Science:

• Computer Vision
• Machine Learning
• Artificial Intelligence

Background:

• Unsupervised domain adaptation (UDA) methods typically handle only two domains, leading to poor performance on new, unseen domains.
• Existing UDA approaches suffer from catastrophic forgetting when adapting to sequential domains, degrading performance on previously learned domains.

Purpose of the Study:

• To propose a new incremental learning framework for domain-incremental UDA.
• To harmonize memorability of old knowledge and discriminability of new knowledge, mimicking human brain's learning process.
• To improve model adaptability and performance on novel target domains without forgetting previous ones.

Main Methods:

• Developed a novel incremental learning framework for domain-incremental unsupervised domain adaptation.
• The framework balances memorability and discriminability to manage knowledge across existing and novel domains.
• Employed a strategy inspired by human brain's learning mechanisms for knowledge retention and acquisition.

Main Results:

• The proposed approach effectively avoids catastrophic forgetting in sequential domain adaptation.
• Performance degradation in previously adapted domains is mitigated.
• Significant improvements in object detection accuracy were observed for novel target domains.

Conclusions:

• The novel incremental learning framework offers a robust solution for domain-incremental UDA.
• The method successfully addresses catastrophic forgetting and performance degradation.
• The approach enhances adaptability and accuracy in evolving domain scenarios.