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| مدل قطعهبندی هر چیزی (Segment Anything Model - SAM)× | ترنسفورمر بینایی× | |
|---|---|---|
| حوزه | یادگیری عمیق | یادگیری عمیق |
| خانواده | Machine learning | Machine learning |
| سال پیدایش≠ | 2023 | 2021 |
| پدیدآور≠ | Alexander Kirillov | Dosovitskiy, A. et al. |
| نوع≠ | Neural network architecture | Transformer architecture for images (self-attention over patches) |
| 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 ↗ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ |
| نامهای دیگر≠ | SAM, Segment Anything | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| مرتبط≠ | 4 | 5 |
| 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. | The Vision Transformer (ViT), introduced by Dosovitskiy and colleagues in 2021, splits an image into fixed-size patches, treats those patches as a sequence, and applies the Transformer self-attention mechanism to image classification. Given enough training data, it surpasses convolutional neural networks (CNNs). |
| ScholarGateمجموعهداده ↗ |
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