Vertaile menetelmiä

	Heikosti valvottu instanssien segmentointi	Kohdetunnistus
Tieteenala	Syväoppiminen	Syväoppiminen
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	2015–2019	2014–2016
Kehittäjä	Multiple contributors (e.g., Hsu et al., Khoreva et al.)	Girshick, R. et al. (R-CNN); Redmon, J. et al. (YOLO)
Tyyppi	Weakly supervised deep learning for pixel-wise instance delineation	Supervised deep learning (region proposal or single-shot)
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.	Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 580–587.
Rinnakkaisnimet	WSIS, weakly-supervised mask prediction, weak-label instance segmentation, box-supervised instance segmentation	visual object detection, image object localization, region-based object detection, bounding-box detection
Liittyvät	6	3
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.	Object detection is a computer vision task in which a deep neural network simultaneously locates and classifies every instance of one or more object categories within an image, producing a bounding box and a class label for each detected object. Modern detectors — from the R-CNN family to YOLO and DETR — achieve near-human accuracy at real-time speeds on standard benchmarks.