Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Spatial-Temporal Graph Convolutional Networks× | Vision Mamba× | |
|---|---|---|
| Ämnesområde | Djupinlärning | Djupinlärning |
| Familj | Machine learning | Machine learning |
| Ursprungsår≠ | 2018 | 2024 |
| Upphovsperson≠ | Sijie Yan | Li Zhu |
| Typ | Neural network architecture | Neural network architecture |
| 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 ↗ |
| Alias | ST-GCN, Spatial-Temporal Graph CNN | ViM, Mamba for Vision |
| Närliggande | 4 | 4 |
| 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. |
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