Semi-supervised object detection has made significant progress with the
development of mean teacher driven self-training. Despite the promising
results, the label mismatch problem is not yet fully explored in the previous
works, leading to severe confirmation bias during self-training. In this paper,
we delve into this problem and propose a simple yet effective LabelMatch
framework from two different yet complementary perspectives, i.e.,
distribution-level and instance-level. For the former one, it is reasonable to
approximate the class distribution of the unlabeled data from that of the
labeled data according to Monte Carlo Sampling. Guided by this weakly
supervision cue, we introduce a re-distribution mean teacher, which leverages
adaptive label-distribution-aware confidence thresholds to generate unbiased
pseudo labels to drive student learning. For the latter one, there exists an
overlooked label assignment ambiguity problem across teacher-student models. To
remedy this issue, we present a novel label assignment mechanism for
self-training framework, namely proposal self-assignment, which injects the
proposals from student into teacher and generates accurate pseudo labels to
match each proposal in the student model accordingly. Experiments on both
MS-COCO and PASCAL-VOC datasets demonstrate the considerable superiority of our
proposed framework to other state-of-the-arts. Code will be available at
this https URL

本篇论文从分布级别和实例级别两个不同但互补的视角提出了一个简单而有效的 LabelMatch 框架，用于解决自我训练期间存在的标签不匹配问题，并通过 Proposal Self-assignment 机制确定准确的伪标签来促进学生和教师之间的匹配。在 MS-COCO 和 PASCAL-VOC 数据集上的实验结果表明，所提出的框架明显优于其他现有方法。