  Semi-supervised object detection has recently achieved substantial progress.
As a mainstream solution, the self-labeling-based methods train the detector on
both labeled data and unlabeled data with pseudo labels predicted by the
detector itself, but their performances are always limited. Through
experimental analysis, we reveal the underlying reason is that the detector is
misguided by the incorrect pseudo labels predicted by itself (dubbed
self-errors). These self-errors can hurt performance even worse than
random-errors, and can be neither discerned nor rectified during the
self-labeling process. In this paper, we propose an effective detection
framework named CrossRectify, to obtain accurate pseudo labels by
simultaneously training two detectors with different initial parameters.
Specifically, the proposed approach leverages the disagreements between
detectors to discern the self-errors and refines the pseudo label quality by
the proposed cross-rectifying mechanism. Extensive experiments show that
CrossRectify achieves outperforming performances over various detector
structures on 2D and 3D detection benchmarks.
