Comparer des méthodes

	Segmentation d'instances adaptative au domaine	Segmentation d'instances
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2018–2021	2017
Auteur d'origine	Chen, Y. et al. (domain-adaptive detection); extended to instance segmentation by multiple groups ~2019–2021	He, K., Gkioxari, G., Dollar, P., Girshick, R.
Type	Domain adaptation + instance segmentation	Pixel-level detection and mask prediction
Source fondatrice	Chen, Y., Li, W., Sakaridis, C., Dai, D., & Van Gool, L. (2018). Domain Adaptive Faster RCNN for Object Detection in the Wild. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3339–3348.	He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2961–2969.
Alias	DA-InstanceSeg, cross-domain instance segmentation, domain adaptation for instance segmentation, unsupervised domain adaptive Mask R-CNN	instance-level segmentation, object instance segmentation, mask prediction, panoptic instance segmentation
Apparentées	3	4
Résumé	Domain-adaptive instance segmentation extends Mask R-CNN-style architectures to operate across distribution shifts — training on a labeled source domain (e.g., synthetic renderings or daytime images) and adapting to an unlabeled or weakly labeled target domain (e.g., real scenes or nighttime footage). Adversarial feature alignment and self-training close the domain gap at both image-level and instance-level granularity.	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.