Compara mètodes

	Segmentació semàntica	Vision Transformer
Camp	Aprenentatge profund	Aprenentatge profund
Família	Machine learning	Machine learning
Any d'origen	2015	2021
Autor original	Long, J., Shelhamer, E., & Darrell, T.	Dosovitskiy, A. et al.
Tipus	Dense prediction / pixel-wise classification	Transformer architecture for images (self-attention over patches)
Font seminal	Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440.	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
Àlies	pixel-wise classification, scene parsing, dense labeling, semantic scene segmentation	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
Relacionats	5	5
Resum	Semantic segmentation assigns a class label to every pixel in an image, producing a dense, category-annotated map of the scene. Unlike object detection, which draws bounding boxes, it delineates the exact spatial extent of each class, making it indispensable in medical imaging, autonomous driving, satellite analysis, and any task where precise region boundaries matter.	The Vision Transformer (ViT), introduced by Dosovitskiy and colleagues in 2021, splits an image into fixed-size patches, treats those patches as a sequence, and applies the Transformer self-attention mechanism to image classification. Given enough training data, it surpasses convolutional neural networks (CNNs).