Source-Free Object Detection via Multi-Source Knowledge Fusion

Source-free domain adaptation (SFDA) adapts a pretrained source model to an unlabeled target domain without accessing source data, which is useful when source data cannot be retained or transferred. Multi-source source-free domain adaptation (MSFDA) further exploits complementary knowledge from multiple pretrained source models, but heterogeneous source-target shifts and noisy pseudo labels make multi-source object detection particularly challenging. We propose a multi-source knowledge-fusion framework that addresses these issues through three complementary mechanisms. First, Text-Driven Feature Augmentation (TFA) transforms unlabeled target images toward each source-domain appearance using only coarse semantic descriptions of the source domains, reducing the mismatch seen by the corresponding source experts. Second, the source experts are locally adapted on their source-stylized target views, while an aggregator integrates their knowledge using adaptively learned contribution weights. Third, mutual confidence selection between the aggregator and domain experts reduces the influence of noisy pseudo labels during self-training. Extensive experiments across multiple real-world domain shifts show consistent improvements over existing SFDA and MSFDA baselines under the standard MSFDA protocol.