Dense Teacher: Dense Pseudo-Labels for Semi-Supervised Object Detection
Hongyu Zhou, Zheng Ge, Songtao Liu, Weixin Mao, Zeming Li, Haiyan Yu, Jian Sun
Abstract
"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. Code will be available."
BibTeX
@inproceedings{eccv2022_denseteacherdens,
title = {Dense Teacher: Dense Pseudo-Labels for Semi-Supervised Object Detection},
author = {Hongyu Zhou and Zheng Ge and Songtao Liu and Weixin Mao and Zeming Li and Haiyan Yu and Jian Sun},
booktitle = {ECCV 2022},
year = {2022}
}