Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Vision Mamba× | Ruimtelijke-Temporele Grafische Convolutie Netwerken× | Transformator voor Visuele Waarneming× | |
|---|---|---|---|
| Vakgebied | Deep learning | Deep learning | Deep learning |
| Familie | Machine learning | Machine learning | Machine learning |
| Jaar van ontstaan≠ | 2024 | 2018 | 2021 |
| Grondlegger≠ | Li Zhu | Sijie Yan | Dosovitskiy, A. et al. |
| Type≠ | Neural network architecture | Neural network architecture | Transformer architecture for images (self-attention over patches) |
| Oorspronkelijke bron≠ | 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. link ↗ | 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). link ↗ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ |
| Aliassen≠ | ViM, Mamba for Vision | ST-GCN, Spatial-Temporal Graph CNN | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| Verwant≠ | 4 | 4 | 5 |
| Samenvatting≠ | 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. | 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). |
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