Porovnat metody

	Vision Mamba	Mamba (model stavového prostoru)	Vision Transformer
Obor	Hluboké učení	Hluboké učení	Hluboké učení
Rodina	Machine learning	Machine learning	Machine learning
Rok vzniku	2024	2023	2021
Tvůrce	Li Zhu	Albert Gu	Dosovitskiy, A. et al.
Typ	Neural network architecture	Neural network architecture	Transformer architecture for images (self-attention over patches)
Původní zdroj	Zhu, L., Liao, B., Zhang, Q., Wang, X., Liu, W., & Wang, X. (2024). Vision Mamba: Efficient state space models for image understanding. In International Conference on Machine Learning.	Gu, A., & Dao, C. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.08956.	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
Další názvy	ViM, Mamba for Vision	Mamba, State space models, Selective state space	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
Příbuzné	4	4	5
Shrnutí	Vision Mamba is an efficient state space model approach for image understanding introduced in 2024 that adapts Mamba, a linear-complexity sequence model, to computer vision. By reformulating image tokens as sequences and using state space models, Vision Mamba achieves competitive accuracy with transformers while maintaining linear computational complexity.	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 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).