To date, the most powerful semi-supervised object detectors (SS-OD) are based
on pseudo-boxes, which need a sequence of post-processing with fine-tuned
hyper-parameters. In this work, we propose replacing the sparse pseudo-boxes
with the dense prediction as a united and straightforward form of pseudo-label.
Compared to the pseudo-boxes, our Dense Pseudo-Label (DPL) does not involve any
post-processing method, thus retaining richer information. We also introduce a
region selection technique to highlight the key information while suppressing
the noise carried by dense labels. We name our proposed SS-OD algorithm that
leverages the DPL as Dense Teacher. On COCO and VOC, Dense Teacher shows
superior performance under various settings compared with the pseudo-box-based
methods.Comment: ECCV202