Semi-Supervised Object Detection via Multi-instance Alignment with Global Class Prototypes

Semi-Supervised object detection (SSOD) aims to improve the generalization ability of object detectors with large-scale unlabeled images. Current pseudo-labeling-based SSOD methods individually learn from labeled data and unlabeled data, without considering the relation be-tween them. To make full use of labeled data, we pro-pose a Multi-instance Alignment model which enhances the prediction consistency based on Global Class Proto-types (MA-GCP). Specifically, we impose the consistency between pseudo ground-truths and their high-IoU candi-dates by minimizing the cross-entropy loss of their class distributions computed based on global class prototypes. These global class prototypes are estimated with the whole labeled dataset via the exponential moving average algorithm. To evaluate the proposed MA-GCP model, we inte-grate it into the state-of-the-art SSOD framework and ex-periments on two benchmark datasets demonstrate the ef-fectiveness of our MA-GCP approach.