DualEnhance: External Multimodal Foundation Models Guidance and Internal Fast-Slow Teacher Regulation

摘要

Source-Free Domain Adaptive Object Detection addresses cross-domain detection on an unlabeled target domain without accessing source data. Existing methods implement self-training with Mean Teacher but are bottlenecked by error accumulation from noisy pseudo-labels generated via recursive teacher-student updates. This issue is handled through the proposed dual enhancements: (1) External Guidance via Multimodal Foundation Models (FMs); (2) Internal Regulation through Fast-Slow Teacher. First, despite FMs' multimodal comprehension, their semantic misalignment with a specific task introduces noise during adaptation. Bidirectional Distillation mitigates this by calibrating the FM using task-specific knowledge transferred from the source detector. The aligned cross-modal knowledge then propagates through high-quality pseudo-label generation. Second, the conventional Mean Teacher suffers from plasticity-stability dilemma, where rapid adaptation corrupts historical knowledge. Fast-Slow Teacher introduces dual-velocity knowledge consolidation: The Fast Teacher dynamically captures emerging domain features, while the Slow Teacher preserves stable historical knowledge and periodically resets the Fast Teacher, establishing an error-correcting dynamic equilibrium. Experiments show our method achieves significant improvements over SOTA.