Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Självövervakad semantisk segmentering× | Vision Transformer× | |
|---|---|---|
| Ämnesområde | Djupinlärning | Djupinlärning |
| Familj | Machine learning | Machine learning |
| Ursprungsår≠ | 2020–2022 | 2021 |
| Upphovsperson≠ | Multiple groups (Caron et al.; Hamilton et al. among key contributors) | Dosovitskiy, A. et al. |
| Typ≠ | Self-supervised dense prediction | Transformer architecture for images (self-attention over patches) |
| Ursprungskälla≠ | Caron, M., Touvron, H., Misra, I., Jegou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021). Emerging Properties in Self-Supervised Vision Transformers. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 9650–9660. DOI ↗ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ |
| Alias | SSL semantic segmentation, unsupervised semantic segmentation, label-free semantic segmentation, self-supervised dense prediction | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| Närliggande | 5 | 5 |
| Sammanfattning≠ | Self-supervised semantic segmentation learns to assign a class label to every pixel of an image without relying on manually annotated segmentation masks. A backbone network is first trained on large quantities of unlabeled images using self-supervised objectives such as contrastive learning or masked image modeling, and the resulting dense features are then used to partition and label image regions, achieving competitive segmentation quality at a fraction of the annotation cost. | 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). |
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