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

6 Jul 2022  ·  HongYu Zhou, Zheng Ge, Songtao Liu, Weixin Mao, Zeming Li, Haiyan Yu, Jian Sun ·

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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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Semi-Supervised Object Detection COCO 100% labeled data Dense Teacher mAP 46.2 # 3
Semi-Supervised Object Detection COCO 10% labeled data Dense Teacher mAP 37.13 # 6
detector FCOS-Res50 # 1
Semi-Supervised Object Detection COCO 5% labeled data Dense Teacher mAP 33.01 # 6


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