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| Puolivalvottu kohteentunnistus× | Esiintymäsegmentointi× | |
|---|---|---|
| Tieteenala | Syväoppiminen | Syväoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2020–2021 | 2017 |
| Kehittäjä≠ | Sohn et al. (STAC); Liu et al. (Unbiased Teacher) | He, K., Gkioxari, G., Dollar, P., Girshick, R. |
| Tyyppi≠ | Semi-supervised learning for detection | Pixel-level detection and mask prediction |
| Alkuperäislähde≠ | Sohn, K., Zhang, Z., Li, C.-L., Zhang, H., Lee, C.-Y., & Pfister, T. (2020). A Simple Semi-Supervised Learning Framework for Object Detection. arXiv preprint arXiv:2005.04757. link ↗ | 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 ↗ |
| Rinnakkaisnimet | SSOD, semi-supervised detection, pseudo-label object detection, label-efficient object detection | instance-level segmentation, object instance segmentation, mask prediction, panoptic instance segmentation |
| Liittyvät≠ | 6 | 4 |
| Tiivistelmä≠ | Semi-supervised object detection trains a detector on a small labeled image set and a large unlabeled image set. A teacher model generates pseudo-labels for unlabeled images, and a student model learns from both real and pseudo-labeled data, dramatically reducing the expensive manual bounding-box annotation burden while achieving accuracy competitive with fully supervised baselines. | 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. |
| ScholarGateAineisto ↗ |
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