Machine learningDeep learning / NLP / CV
领域自适应实例分割
领域自适应实例分割将Mask R-CNN风格的架构扩展到跨分布偏移操作——在带标签的源域（例如，合成渲染或白天图像）上训练，并适应无标签或弱标签的目标域（例如，真实场景或夜间镜头）。对抗性特征对齐和自训练在图像级别和实例级别粒度上弥合了领域差距。
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来源
- 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 ↗
如何引用本页
ScholarGate. (2026, June 3). Domain-Adaptive Instance Segmentation (Cross-Domain Instance-Level Pixel Segmentation). ScholarGate. https://scholargate.app/zh/deep-learning/domain-adaptive-instance-segmentation
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