Σύγκριση μεθόδων

	Μοντέλο Τμηματοποίησης Οτιδήποτε	Vision Transformer
Πεδίο	Βαθιά Μάθηση	Βαθιά Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2023	2021
Δημιουργός	Alexander Kirillov	Dosovitskiy, A. et al.
Τύπος	Neural network architecture	Transformer architecture for images (self-attention over patches)
Θεμελιώδης πηγή	Kirillov, A., Mintun, E., Darrell, T., & Girshick, R. (2023). Segment Anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 4015-4026).	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
Εναλλακτικές ονομασίες	SAM, Segment Anything	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
Συναφείς	4	5
Σύνοψη	Segment Anything Model (SAM) is a foundation model introduced by Kirillov et al. in 2023 that can segment any object in an image given various forms of prompts. SAM is trained on a massive dataset of diverse images and learns to segment objects based on minimal user input such as points, boxes, or text descriptions.	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).