Domain-adaptive Instance Segmentation
Domain-adaptive instance segmentation extends architectures of the Mask R-CNN style 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.
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Method map
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Источники
- 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: 10.1109/CVPR.2018.00352 ↗
- VS, V., Gupta, V., Oza, P., Sindagi, V. A., & Patel, V. M. (2021). MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 4516–4526. DOI: 10.1109/CVPR46437.2021.00449 ↗
Как цитировать эту страницу
ScholarGate. (2026, June 3). Domain-Adaptive Instance Segmentation (Cross-Domain Instance-Level Pixel Segmentation). ScholarGate. https://scholargate.app/ru/deep-learning/domain-adaptive-instance-segmentation
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Сегментация экземпляровГлубокое обучение↔ compare
- Семантическая сегментацияГлубокое обучение↔ compare
- Трансферное обучение с сегментацией экземпляровГлубокое обучение↔ compare
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