Vertaile menetelmiä

	Spatiaalis-temporaaliset graafikonvoluutioverkot	Vision Transformer
Tieteenala	Syväoppiminen	Syväoppiminen
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	2018	2021
Kehittäjä	Sijie Yan	Dosovitskiy, A. et al.
Tyyppi	Neural network architecture	Transformer architecture for images (self-attention over patches)
Alkuperäislähde	Yan, S., Xiong, Y., & Lin, D. (2018). Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 32).	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
Rinnakkaisnimet	ST-GCN, Spatial-Temporal Graph CNN	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
Liittyvät	4	5
Tiivistelmä	Spatial-Temporal Graph Convolutional Networks (ST-GCN) is an architecture introduced by Yan et al. in 2018 for skeleton-based action recognition. By modeling human skeletons as graphs where joints are nodes and bones are edges, ST-GCN applies graph convolutions across space and time to recognize actions from skeleton sequences.	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).