Region Confidence Refinement with Progressive Semantic Mining for Source-Free Domain Adaptive Object Detection

Source-Free Domain Adaptive (SFDA) object detection addresses the challenges of detection in scenarios where both source domain data and target domain labels are unavailable. Due to the lack of data supervision, pseudo label learning has become the key to SFDA object detection. However, prevailing SFDA methods primarily concentrate on pseudo labels exhibiting exceptionally high or low confidence, without simultaneously considering false negative samples in high confidence and false positive samples in low confidence. We summarize this issue as the double-sided problem of pseudo labels. To address this issue, we propose the Region Confidence Refinement (RCR) aimed at refining the quality of pseudo labels via progressive semantic mining. Specifically, we bolster the semantic representation capacity of the detector across both pixel and image levels. Firstly, we design the Multi-Channel Style Filter (MSF) module to enrich pixel-level semantic representation by eliminating background-induced noise. Secondly, we design the Cross-Modal Semantic Enhancement (CSE) to enhance the classification efficacy of the detector amidst supervised information scarcity by aligning textual and image features, thereby amplifying image-level semantic representation. Finally, we design a Semantic Aggregation Strategy (SAS) for reconstructing region-level confidence. Extensive experiments demonstrate our proposed RCR achieves the state-of-the-art (SOTA) performance.