방법 비교

	약지도 인스턴스 분할	약한 지도 의미론적 분할
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2015–2019	2014–2016
창시자	Multiple contributors (e.g., Hsu et al., Khoreva et al.)	Multiple contributors; Class Activation Mapping (Zhou et al., 2016) is foundational
유형	Weakly supervised deep learning for pixel-wise instance delineation	Pixel-level classification with image-level or coarse supervision
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.	Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning Deep Features for Discriminative Localization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2921–2929.
별칭	WSIS, weakly-supervised mask prediction, weak-label instance segmentation, box-supervised instance segmentation	WSSS, weak-label segmentation, image-level supervised segmentation, weakly-labeled pixel classification
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.	Weakly Supervised Semantic Segmentation (WSSS) trains pixel-level scene parsers using only cheap, coarse annotations — typically image-level class tags — instead of costly dense pixel masks. By generating proxy pseudo-labels from a classification network (via Class Activation Maps or similar localisation cues) and iteratively refining them, WSSS brings full-supervision accuracy within reach at a fraction of the annotation cost.