So sánh phương pháp

	Phân đoạn ngữ nghĩa tinh chỉnh	Phân đoạn thực thể (Instance Segmentation)
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2015–2018	2017
Người khởi xướng	Long, Shelhamer & Darrell (FCN); Chen et al. (DeepLab)	He, K., Gkioxari, G., Dollar, P., Girshick, R.
Loại	Transfer learning / dense prediction	Pixel-level detection and mask prediction
Công trình gốc	Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3431–3440.	He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2961–2969.
Tên gọi khác	fine-tuned semseg, domain-adapted semantic segmentation, transfer learning semantic segmentation, pretrained dense prediction fine-tuning	instance-level segmentation, object instance segmentation, mask prediction, panoptic instance segmentation
Liên quan	4	4
Tóm tắt	Fine-tuned semantic segmentation adapts a deep neural network pre-trained on a large pixel-labelled dataset (e.g., ImageNet-pretrained backbone with an encoder-decoder head trained on COCO or Cityscapes) to a new target domain by continuing training on domain-specific annotated images. The result is a model that assigns a class label to every pixel in an image while leveraging rich visual representations learned from vastly more data than the target domain alone could provide.	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.