방법 비교

	픽셀 단위의 수동 주석 마스크에 의존하지 않고 이미지의 모든 픽셀에 클래스 레이블을 할당하도록 학습하는 자기 지도 의미론적 분할.	인스턴스 분할
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2020–2022	2017
창시자	Multiple groups (Caron et al.; Hamilton et al. among key contributors)	He, K., Gkioxari, G., Dollar, P., Girshick, R.
유형	Self-supervised dense prediction	Pixel-level detection and mask prediction
원전	Caron, M., Touvron, H., Misra, I., Jegou, 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.	He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2961–2969.
별칭	SSL semantic segmentation, unsupervised semantic segmentation, label-free semantic segmentation, self-supervised dense prediction	instance-level segmentation, object instance segmentation, mask prediction, panoptic instance segmentation
관련	5	4
요약	Self-supervised semantic segmentation learns to assign a class label to every pixel of an image without relying on manually annotated segmentation masks. A backbone network is first trained on large quantities of unlabeled images using self-supervised objectives such as contrastive learning or masked image modeling, and the resulting dense features are then used to partition and label image regions, achieving competitive segmentation quality at a fraction of the annotation cost.	Instance segmentation is a computer vision task that simultaneously detects every distinct object in an image and produces a precise pixel-level mask for each individual object instance. Unlike semantic segmentation, which labels every pixel with a class, instance segmentation distinguishes between separate objects of the same class, enabling fine-grained spatial understanding.