Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Сегментация экземпляров× | Vision Transformer× | |
|---|---|---|
| Область | Глубокое обучение | Глубокое обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2017 | 2021 |
| Автор метода≠ | He, K., Gkioxari, G., Dollar, P., Girshick, R. | Dosovitskiy, A. et al. |
| Тип≠ | Pixel-level detection and mask prediction | Transformer architecture for images (self-attention over patches) |
| Основополагающий источник≠ | He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2961–2969. DOI ↗ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ |
| Другие названия | instance-level segmentation, object instance segmentation, mask prediction, panoptic instance segmentation | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| Связанные≠ | 4 | 5 |
| Сводка≠ | Instance segmentation is a computer vision task that simultaneously detects every distinct object in an image and produces a precise pixel-level mask for each individual object instance. Unlike semantic segmentation, which labels every pixel with a class, instance segmentation distinguishes between separate objects of the same class, enabling fine-grained spatial understanding. | 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). |
| ScholarGateНабор данных ↗ |
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