Compara mètodes

	Mamba (Model d'Espai d'Estats)	Xarxes Convolucionals de Graf Espaciotemporal	Swin Transformer
Camp	Aprenentatge profund	Aprenentatge profund	Aprenentatge profund
Família	Machine learning	Machine learning	Machine learning
Any d'origen	2023	2018	2021
Autor original	Albert Gu	Sijie Yan	Ze Liu
Tipus	Neural network architecture	Neural network architecture	Neural network architecture
Font seminal	Gu, A., & Dao, C. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.08956.	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).	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).
Àlies	Mamba, State space models, Selective state space	ST-GCN, Spatial-Temporal Graph CNN	Swin, Hierarchical Vision Transformer
Relacionats	4	4	4
Resum	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.	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 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.