Comparer des méthodes

	Segmentation d'instances	Vision Transformer
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2017	2021
Auteur d'origine	He, K., Gkioxari, G., Dollar, P., Girshick, R.	Dosovitskiy, A. et al.
Type	Pixel-level detection and mask prediction	Transformer architecture for images (self-attention over patches)
Source fondatrice	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.
Alias	instance-level segmentation, object instance segmentation, mask prediction, panoptic instance segmentation	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
Apparentées	4	5
Résumé	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).