Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Модель сегментації всього (Segment Anything Model)× | Vision Mamba× | |
|---|---|---|
| Галузь | Глибоке навчання | Глибоке навчання |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 2023 | 2024 |
| Автор методу≠ | Alexander Kirillov | Li Zhu |
| Тип | Neural network architecture | Neural network architecture |
| Основоположне джерело≠ | 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 ↗ |
| Інші назви | SAM, Segment Anything | ViM, Mamba for Vision |
| Пов'язані | 4 | 4 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
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