So sánh phương pháp

	Phát hiện đối tượng tự giám sát	Phát hiện đối tượng bán giám sát
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2019–2021	2020–2021
Người khởi xướng	He et al. (MoCo); Caron et al. (DINO); Henaff et al. (DetCon)	Sohn et al. (STAC); Liu et al. (Unbiased Teacher)
Loại	Self-supervised pre-training + supervised fine-tuning	Semi-supervised learning for detection
Công trình gốc	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.	Sohn, K., Zhang, Z., Li, C.-L., Zhang, H., Lee, C.-Y., & Pfister, T. (2020). A Simple Semi-Supervised Learning Framework for Object Detection. arXiv preprint arXiv:2005.04757.
Tên gọi khác	SSL object detection, self-supervised detection, unsupervised pre-training for detection, contrastive pre-training for detection	SSOD, semi-supervised detection, pseudo-label object detection, label-efficient object detection
Liên quan	4	6
Tóm tắt	Self-supervised object detection uses unlabeled image data to pre-train a visual backbone through pretext tasks such as contrastive learning or masked image modeling, then fine-tunes the backbone with a detection head on a smaller labeled dataset. This approach dramatically reduces reliance on expensive bounding-box annotations while matching or approaching fully supervised detection performance.	Semi-supervised object detection trains a detector on a small labeled image set and a large unlabeled image set. A teacher model generates pseudo-labels for unlabeled images, and a student model learns from both real and pseudo-labeled data, dramatically reducing the expensive manual bounding-box annotation burden while achieving accuracy competitive with fully supervised baselines.