Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Eneseteadlik instantségmenteerimine× | Vision Transformer× | |
|---|---|---|
| Valdkond | Süvaõpe | Süvaõpe |
| Perekond | Machine learning | Machine learning |
| Tekkeaasta≠ | 2021–2022 | 2021 |
| Looja≠ | Wang et al. (FreeSOLO); Caron et al. (DINO) | Dosovitskiy, A. et al. |
| Tüüp≠ | Self-supervised deep learning for pixel-level object delineation | Transformer architecture for images (self-attention over patches) |
| Algallikas≠ | Wang, X., Zhu, Z., Cao, G., Yao, Z., Jiang, Z., & Ye, J. (2022). FreeSOLO: Learning to Segment Objects without Annotations. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 14176–14186. link ↗ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ |
| Rööpnimetused | SSIS, unsupervised instance segmentation, label-free instance segmentation, self-supervised mask prediction | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| Seotud≠ | 4 | 5 |
| Kokkuvõte≠ | Self-supervised instance segmentation learns to detect and delineate individual object instances in images without any human-annotated masks or bounding boxes. Instead of relying on costly pixel-level labels, it exploits self-supervised pretraining, multi-view consistency, and pseudo-label generation to discover and segment objects purely from raw image data. | 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). |
| ScholarGateAndmestik ↗ |
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