Vertaile menetelmiä

	Siirto-oppiminen ja instanssisegmentointi	Esiintymäsegmentointi
Tieteenala	Syväoppiminen	Syväoppiminen
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	2017 (Mask R-CNN); transfer learning paradigm: 2010	2017
Kehittäjä	He, K. et al. (Mask R-CNN); transfer learning framework: Pan & Yang	He, K., Gkioxari, G., Dollar, P., Girshick, R.
Tyyppi	Transfer learning applied to instance segmentation	Pixel-level detection and mask prediction
Alkuperäislähde	He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2961–2969.	He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2961–2969.
Rinnakkaisnimet	pretrained instance segmentation, fine-tuned Mask R-CNN, transfer learning for panoptic segmentation, domain-adapted instance segmentation	instance-level segmentation, object instance segmentation, mask prediction, panoptic instance segmentation
Liittyvät	4	4
Tiivistelmä	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.	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.