Comparer des méthodes

	Segmentation d'instances faiblement supervisée	Segmentation d'instances
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2015–2019	2017
Auteur d'origine	Multiple contributors (e.g., Hsu et al., Khoreva et al.)	He, K., Gkioxari, G., Dollar, P., Girshick, R.
Type	Weakly supervised deep learning for pixel-wise instance delineation	Pixel-level detection and mask prediction
Source fondatrice	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.
Alias	WSIS, weakly-supervised mask prediction, weak-label instance segmentation, box-supervised instance segmentation	instance-level segmentation, object instance segmentation, mask prediction, panoptic instance segmentation
Apparentées	6	4
Résumé	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.