手法を比較

	半教師ありインスタンスセグメンテーション	インスタンスセグメンテーション
分野	深層学習	深層学習
系統	Machine learning	Machine learning
提唱年	2018–2021	2017
提唱者	Multiple independent research groups (2018–2021)	He, K., Gkioxari, G., Dollar, P., Girshick, R.
種類	Semi-supervised deep learning for dense prediction	Pixel-level detection and mask prediction
原典	Hu, H., Wei, P., Zheng, H., Bai, X., Wei, Y., & Chen, Y. (2021). Semi-supervised Semantic Segmentation via Adaptive Equalization Learning. Advances in Neural Information Processing Systems (NeurIPS), 34, 22106–22118.	He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2961–2969.
別名	Semi-supervised Mask R-CNN, pseudo-label instance segmentation, label-efficient instance segmentation, SSIS	instance-level segmentation, object instance segmentation, mask prediction, panoptic instance segmentation
関連	6	4
概要	Semi-supervised instance segmentation trains a model to detect and delineate every object instance in an image using a small labeled set and a large unlabeled image corpus. By generating pseudo-labels from confident predictions on unlabeled images and enforcing consistency under augmentation, the approach achieves competitive mask accuracy at a fraction of the full 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.