Refining Pseudo Labeling via Multi-Granularity Confidence Alignment for Unsupervised Cross Domain Object Detection

Abstract

Most state-of-the-art object detection methods suffer from poor generalization due to the domain shift between the training and testing datasets. To resolve this challenge, unsupervised cross domain object detection is proposed to learn an object detector for an unlabeled target domain by transferring knowledge from an annotated source domain. Promising results have been achieved via Mean Teacher, however, pseudo labeling which is the bottleneck of mutual learning remains to be further explored. In this study, we find that confidence misalignment of the predictions, including category-level overconfidence, instance-level task confidence inconsistency, and image-level confidence misfocusing, leading to the injection of noisy pseudo labels in the training process, will bring suboptimal performance. Considering the above issue, we present a novel general framework termed Multi-Granularity Confidence Alignment Mean Teacher (MGCAMT) for unsupervised cross domain object detection, which alleviates confidence misalignment across category-, instance-, and image-levels simultaneously to refine pseudo labeling for better teacher-student learning. Specifically, to align confidence with accuracy at category level, we propose Classification Confidence Alignment (CCA) to model category uncertainty based on Evidential Deep Learning (EDL) and filter out the category incorrect labels via an uncertainty-aware selection strategy. Furthermore, we design Task Confidence Alignment (TCA) to mitigate the instance-level misalignment between classification and localization by enabling each classification feature to adaptively identify the optimal feature for regression. Finally, we develop imagery Focusing Confidence Alignment (FCA) adopting another way of pseudo label learning, i.e., we use the original outputs from the Mean Teacher network for supervised learning without label assignment to achieve a balanced perception of the image’s spatial layout. When these three procedures are integrated into a single framework, they mutually benefit to improve the final performance from a cooperative learning perspective. Extensive experiments across multiple scenarios demonstrate that our method outperforms large foundational models, and surpasses other state-of-the-art approaches by a large margin.