Сравнение на методи

	Самообучаващо се детектиране на обекти	Детекция на обекти
Област	Дълбоко обучение	Дълбоко обучение
Семейство	Machine learning	Machine learning
Година на възникване	2019–2021	2014–2016
Създател	He et al. (MoCo); Caron et al. (DINO); Henaff et al. (DetCon)	Girshick, R. et al. (R-CNN); Redmon, J. et al. (YOLO)
Тип	Self-supervised pre-training + supervised fine-tuning	Supervised deep learning (region proposal or single-shot)
Основополагащ източник	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.	Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 580–587.
Други названия	SSL object detection, self-supervised detection, unsupervised pre-training for detection, contrastive pre-training for detection	visual object detection, image object localization, region-based object detection, bounding-box detection
Свързани	4	3
Резюме	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.	Object detection is a computer vision task in which a deep neural network simultaneously locates and classifies every instance of one or more object categories within an image, producing a bounding box and a class label for each detected object. Modern detectors — from the R-CNN family to YOLO and DETR — achieve near-human accuracy at real-time speeds on standard benchmarks.