Not All Classes are Equal: Adaptively Focus-Aware Confidence for Semi-Supervised Object Detection

Hui Zhu, Yongchun Lü, Hongyu Zhao, Guoqing Zhao, Xiaofang Zhao

Abstract: Semi-supervised object detection (SSOD) is a significant application of Semi-supervised learning to further improve object detectors but suffers more seriously from confirmation bias and error accumulation caused by the classes imbalance. Existing SSOD approaches have attempted to address this issue but fails to consider dynamically changed detection difficulties of different classes for detectors. In this paper, we propose adaptively focus-aware confidence, which treats object classes differently. Predictions generated from the teacher and student models are stored in a memory dictionary, and the differences between them are utilized to adaptively perceive learning statuses. Based on this, confidence thresholds are flexibly assigned and adjusted for different classes. Extensive experiments are conducted on MS-COCO benchmark dataset with multiple protocols and our SSOD framework out-performs the state-of-the-art competitors by a large margin.