Sammenlign metoder

	Spatial-Temporal Graph Convolutional Networks	Mamba (tilstandsromsmodell)	Swin Transformer
Fagfelt	Dyp læring	Dyp læring	Dyp læring
Familie	Machine learning	Machine learning	Machine learning
Opprinnelsesår	2018	2023	2021
Opphavsperson	Sijie Yan	Albert Gu	Ze Liu
Type	Neural network architecture	Neural network architecture	Neural network architecture
Opprinnelig kilde	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).	Gu, A., & Dao, C. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.08956.	Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021). Swin Transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 10012-10022).
Alias	ST-GCN, Spatial-Temporal Graph CNN	Mamba, State space models, Selective state space	Swin, Hierarchical Vision Transformer
Relaterte	4	4	4
Sammendrag	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.	Mamba is a sequence model architecture introduced by Gu and Dao in 2023 that achieves linear-time complexity while maintaining strong performance on language modeling tasks. By combining state space models with input-dependent selectivity, Mamba addresses the quadratic complexity of transformers while preserving modeling power.	The Swin Transformer is a hierarchical vision transformer introduced by Liu et al. in 2021 that uses shifted window attention to achieve computational efficiency while maintaining strong performance on computer vision tasks. Unlike the original Vision Transformer which applies global self-attention, Swin uses local window-based attention with periodic shifting to balance expressiveness and efficiency.