@inproceedings{ed9d18cbc1984272ab1ee5cde30245fd,
title = "Dense Teacher: Dense Pseudo-Labels for Semi-supervised Object Detection",
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 is available at https://github.com/Megvii-BaseDetection/DenseTeacher.",
keywords = "Dense pseudo-label, 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",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 17th European Conference on Computer Vision, ECCV 2022 ; Conference date: 23-10-2022 Through 27-10-2022",
year = "2022",
doi = "10.1007/978-3-031-20077-9\_3",
language = "英语",
isbn = "9783031200762",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "35--50",
editor = "Shai Avidan and Gabriel Brostow and Moustapha Ciss{\'e} and Farinella, \{Giovanni Maria\} and Tal Hassner",
booktitle = "Computer Vision – ECCV 2022 - 17th European Conference, Proceedings",
address = "德国",
}