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.
H. Zhou and Z. Ge—Authors contributed equally to this work.
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Notes
- 1.
See also in Sect. 3.2 for a related discussion.
- 2.
CrowdHuman is a benchmark for detecting humans in a crowded situation, performance is measured by Log-average Miss Rate (mMR). The lower the better.
- 3.
“Anchor” stands for “anchor point” in anchor-free detectors and “anchor box” in anchor-based detectors.
- 4.
COCO20cls is the sampled COCO train2017 set, only 20 classes same as in VOC are included.
- 5.
Since the “unlabeled images” under the COCO-Standard setting actually come with annotations, we can perform label assignments on images using these annotations. The difference between our division (\(k=1\)) and the assigned foreground is defined as hard negatives.
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Zhou, H. et al. (2022). Dense Teacher: Dense Pseudo-Labels for Semi-supervised Object Detection. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13669. Springer, Cham. https://doi.org/10.1007/978-3-031-20077-9_3
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