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| Domenski adaptivna segmentacija instanci× | Prenosno učenje sa segmentacijom instanci× | |
|---|---|---|
| Oblast | Duboko učenje | Duboko učenje |
| Porodica | Machine learning | Machine learning |
| Godina nastanka≠ | 2018–2021 | 2017 (Mask R-CNN); transfer learning paradigm: 2010 |
| Tvorac≠ | Chen, Y. et al. (domain-adaptive detection); extended to instance segmentation by multiple groups ~2019–2021 | He, K. et al. (Mask R-CNN); transfer learning framework: Pan & Yang |
| Tip≠ | Domain adaptation + instance segmentation | Transfer learning applied to instance segmentation |
| Temeljni izvor≠ | 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 ↗ |
| Drugi nazivi | DA-InstanceSeg, cross-domain instance segmentation, domain adaptation for instance segmentation, unsupervised domain adaptive Mask R-CNN | pretrained instance segmentation, fine-tuned Mask R-CNN, transfer learning for panoptic segmentation, domain-adapted instance segmentation |
| Srodne≠ | 3 | 4 |
| Sažetak≠ | 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. | Transfer learning with instance segmentation reuses a backbone convolutional network pretrained on a large image corpus (typically ImageNet or COCO) as the feature extractor for an instance segmentation model such as Mask R-CNN, then fine-tunes the full pipeline on a smaller target dataset. This approach delivers state-of-the-art per-object mask accuracy with a fraction of the labeled data and compute that training from scratch would require. |
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