Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Vision Mamba× | Mamba (tilstandsromsmodell)× | Spatial-Temporal Graph Convolutional Networks× | |
|---|---|---|---|
| Fagfelt | Dyp læring | Dyp læring | Dyp læring |
| Familie | Machine learning | Machine learning | Machine learning |
| Opprinnelsesår≠ | 2024 | 2023 | 2018 |
| Opphavsperson≠ | Li Zhu | Albert Gu | Sijie Yan |
| Type | Neural network architecture | Neural network architecture | Neural network architecture |
| Opprinnelig kilde≠ | 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 ↗ | Gu, A., & Dao, C. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.08956. 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 ↗ |
| Alias≠ | ViM, Mamba for Vision | Mamba, State space models, Selective state space | ST-GCN, Spatial-Temporal Graph CNN |
| Relaterte | 4 | 4 | 4 |
| Sammendrag≠ | 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. | 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. |
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