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| 도메인 적응 인스턴스 분할× | 인스턴스 분할× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2018–2021 | 2017 |
| 창시자≠ | Chen, Y. et al. (domain-adaptive detection); extended to instance segmentation by multiple groups ~2019–2021 | He, K., Gkioxari, G., Dollar, P., Girshick, R. |
| 유형≠ | Domain adaptation + instance segmentation | Pixel-level detection and mask prediction |
| 원전≠ | 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. DOI ↗ | He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2961–2969. DOI ↗ |
| 별칭 | 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 |
| 관련≠ | 3 | 4 |
| 요약≠ | 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. |
| ScholarGate데이터셋 ↗ |
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