Dense Teacher: Dense Pseudo-Labels for Semi-supervised Object Detection

• URL: http://arxiv.org/abs/2207.02541v1
• Date: Wed, 6 Jul 2022 09:41:17 GMT
• Title: Dense Teacher: Dense Pseudo-Labels for Semi-supervised Object Detection
• Authors: Hongyu Zhou, Zheng Ge, Songtao Liu, Weixin Mao, Zeming Li, Haiyan Yu, Jian Sun
• Abstract summary: 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 name our proposed SS-OD algorithm that leverages the DPL as Dense Teacher.
• Score: 83.8770773275045
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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