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| Eneseteadlik instantségmenteerimine× | Enesest juhendav õppimine× | |
|---|---|---|
| Valdkond≠ | Süvaõpe | Masinõpe |
| Perekond | Machine learning | Machine learning |
| Tekkeaasta≠ | 2021–2022 | 2018–2020 |
| Looja≠ | Wang et al. (FreeSOLO); Caron et al. (DINO) | LeCun, Y. and community (formalized ~2018–2020) |
| Tüüp≠ | Self-supervised deep learning for pixel-level object delineation | Representation learning paradigm |
| 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 ↗ | LeCun, Y. & Misra, I. (2022). Self-supervised learning: The dark matter of intelligence. Meta AI Blog. https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/ link ↗ |
| Rööpnimetused | SSIS, unsupervised instance segmentation, label-free instance segmentation, self-supervised mask prediction | SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning |
| Seotud≠ | 4 | 3 |
| 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. | Self-supervised learning (SSL) is a machine-learning paradigm that generates its own supervisory signal directly from unlabeled data by defining an auxiliary pretext task — such as predicting masked words, rotating images, or contrasting augmented views — and uses the learned representations as a powerful starting point for downstream tasks with minimal labeled examples. |
| ScholarGateAndmestik ↗ |
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