مقایسهٔ روشها
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| مدل قطعهبندی هر چیزی (Segment Anything Model - SAM)× | ویژن مامبا× | |
|---|---|---|
| حوزه | یادگیری عمیق | یادگیری عمیق |
| خانواده | Machine learning | Machine learning |
| سال پیدایش≠ | 2023 | 2024 |
| پدیدآور≠ | Alexander Kirillov | Li Zhu |
| نوع | Neural network architecture | Neural network architecture |
| منبع بنیادین≠ | Kirillov, A., Mintun, E., Darrell, T., & Girshick, R. (2023). Segment Anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 4015-4026). DOI ↗ | 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 ↗ |
| نامهای دیگر | SAM, Segment Anything | ViM, Mamba for Vision |
| مرتبط | 4 | 4 |
| خلاصه≠ | Segment Anything Model (SAM) is a foundation model introduced by Kirillov et al. in 2023 that can segment any object in an image given various forms of prompts. SAM is trained on a massive dataset of diverse images and learns to segment objects based on minimal user input such as points, boxes, or text descriptions. | 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. |
| ScholarGateمجموعهداده ↗ |
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