Krahasoni metodat

	Segmentim i instancave	Vision Transformer
Fusha	Mësimi i thellë	Mësimi i thellë
Familja	Machine learning	Machine learning
Viti i origjinës	2017	2021
Krijuesi	He, K., Gkioxari, G., Dollar, P., Girshick, R.	Dosovitskiy, A. et al.
Lloji	Pixel-level detection and mask prediction	Transformer architecture for images (self-attention over patches)
Burimi themelues	He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2961–2969.	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
Emërtime të tjera	instance-level segmentation, object instance segmentation, mask prediction, panoptic instance segmentation	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
Të lidhura	4	5
Përmbledhja	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.	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).