Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Spatial-Temporal Graph Convolutional Networks× | Vision Mamba× | Vision Transformer× | |
|---|---|---|---|
| Ämnesområde | Djupinlärning | Djupinlärning | Djupinlärning |
| Familj | Machine learning | Machine learning | Machine learning |
| Ursprungsår≠ | 2018 | 2024 | 2021 |
| Upphovsperson≠ | Sijie Yan | Li Zhu | Dosovitskiy, A. et al. |
| Typ≠ | Neural network architecture | Neural network architecture | Transformer architecture for images (self-attention over patches) |
| Ursprungskälla≠ | 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 ↗ | 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 ↗ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ |
| Alias≠ | ST-GCN, Spatial-Temporal Graph CNN | ViM, Mamba for Vision | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| Närliggande≠ | 4 | 4 | 5 |
| Sammanfattning≠ | 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. | 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. | 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). |
| ScholarGateDatamängd ↗ |
|
|
|