Vertaile menetelmiä

	Heikosti valvottu instanssien segmentointi	Esiintymäsegmentointi
Tieteenala	Syväoppiminen	Syväoppiminen
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	2015–2019	2017
Kehittäjä	Multiple contributors (e.g., Hsu et al., Khoreva et al.)	He, K., Gkioxari, G., Dollar, P., Girshick, R.
Tyyppi	Weakly supervised deep learning for pixel-wise instance delineation	Pixel-level detection and mask prediction
Alkuperäislähde	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.	He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2961–2969.
Rinnakkaisnimet	WSIS, weakly-supervised mask prediction, weak-label instance segmentation, box-supervised instance segmentation	instance-level segmentation, object instance segmentation, mask prediction, panoptic instance segmentation
Liittyvät	6	4
Tiivistelmä	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.