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| 약지도 인스턴스 분할× | 인스턴스 분할× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2015–2019 | 2017 |
| 창시자≠ | Multiple contributors (e.g., Hsu et al., Khoreva et al.) | He, K., Gkioxari, G., Dollar, P., Girshick, R. |
| 유형≠ | Weakly supervised deep learning for pixel-wise instance delineation | Pixel-level detection and mask prediction |
| 원전≠ | Hsu, C.-C., Hsu, K.-J., Tsai, C.-C., Lin, Y.-Y., & Chuang, Y.-Y. (2019). Weakly supervised instance segmentation using the bounding box tightness prior. Advances in Neural Information Processing Systems (NeurIPS), 32. 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 ↗ |
| 별칭 | WSIS, weakly-supervised mask prediction, weak-label instance segmentation, box-supervised instance segmentation | instance-level segmentation, object instance segmentation, mask prediction, panoptic instance segmentation |
| 관련≠ | 6 | 4 |
| 요약≠ | Weakly supervised instance segmentation trains deep networks to delineate individual object instances at pixel level using only cheap, incomplete annotations — such as bounding boxes, image-level labels, or point clicks — rather than costly full pixel-wise masks. It dramatically reduces annotation effort while still producing instance-level masks for each object in an image. | 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. |
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