পদ্ধতির তুলনা করুন

	স্থানিক-কালিক গ্রাফ কনভল্যুশনাল নেটওয়ার্ক	ভিশন মাম্বা
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2018	2024
প্রবর্তক	Sijie Yan	Li Zhu
ধরন	Neural network architecture	Neural network architecture
মৌলিক উৎস	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).	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.
অপর নাম	ST-GCN, Spatial-Temporal Graph CNN	ViM, Mamba for Vision
সম্পর্কিত	4	4
সারসংক্ষেপ	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.