Machine learningDeep learning / NLP / CV
自监督目标检测
自监督目标检测利用未标注图像数据，通过对比学习或掩码图像建模等预训练任务，对视觉骨干网络进行预训练，然后在一个较小的标注数据集上，用检测头对骨干网络进行微调。这种方法显著减少了对昂贵边界框标注的依赖，同时能达到或接近全监督检测的性能。
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来源
- He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020). Momentum Contrast for Unsupervised Visual Representation Learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 9729–9738. DOI: 10.1109/CVPR42600.2020.00975 ↗
- Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021). Emerging Properties in Self-Supervised Vision Transformers. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 9650–9660. DOI: 10.1109/ICCV48922.2021.00951 ↗
如何引用本页
ScholarGate. (2026, June 3). Self-supervised Pre-training for Object Detection. ScholarGate. https://scholargate.app/zh/deep-learning/self-supervised-object-detection
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