Rethinking Pseudo-Labels: Multi-Granularity Supervision for Domain Adaptive Object Detection

Domain Adaptive Object Detection (DAOD) adapts detec-tors from labeled source domains to unlabeled target domains. Currentteacher-student methods rely solely on proposal-level pseudo-boxes assupervision, which suffer from substantial noise under domain shift. Wemake an intriguing empirical observation: image-level predictions, ob-tained by aggregating proposal scores, achieve significantly higher ac-curacy than proposal-level pseudo-boxes. This superiority stems fromaggregation’s inherent noise reduction and robustness to localization er-rors. Despite this finding, image-level predictions have been completelyoverlooked as a supervision source in DAOD, though extensively usedin weakly supervised detection. We propose Multi-Granularity Pseudo-Labeling (MGPL), which for the first time incorporates image-level su-pervision alongside proposal-level pseudo-boxes in teacher-student DAOD.Our framework supervises the student detector at both granularities:proposal-level boxes provide spatial precision while image-level predic-tions offer reliable category information. We further introduce coarse-to-fine collaborative filtering, where image-level predictions guide class-adaptive threshold selection for proposals, reducing false negatives whilemaintaining precision. Extensive experiments demonstrate state-of-the-art performance across multiple benchmarks. For example, on PascalVOC → Clipart1k, we achieve 51.4% mAP, surpassing previous best andfully-supervised Oracle by 2.3% and 6.4% respectively. Our work revealsthe untapped potential of multi-granularity learning in domain adapta-tion. Code will be made publicly available.