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	Segmentazione di istanze debolmente supervisionata	Segmentazione di istanze auto-supervisionata
Campo	Apprendimento profondo	Apprendimento profondo
Famiglia	Machine learning	Machine learning
Anno di origine	2015–2019	2021–2022
Ideatore	Multiple contributors (e.g., Hsu et al., Khoreva et al.)	Wang et al. (FreeSOLO); Caron et al. (DINO)
Tipo	Weakly supervised deep learning for pixel-wise instance delineation	Self-supervised deep learning for pixel-level object delineation
Fonte seminale	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.	Wang, X., Zhu, Z., Cao, G., Yao, Z., Jiang, Z., & Ye, J. (2022). FreeSOLO: Learning to Segment Objects without Annotations. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 14176–14186.
Alias	WSIS, weakly-supervised mask prediction, weak-label instance segmentation, box-supervised instance segmentation	SSIS, unsupervised instance segmentation, label-free instance segmentation, self-supervised mask prediction
Correlati	6	4
Sintesi	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.	Self-supervised instance segmentation learns to detect and delineate individual object instances in images without any human-annotated masks or bounding boxes. Instead of relying on costly pixel-level labels, it exploits self-supervised pretraining, multi-view consistency, and pseudo-label generation to discover and segment objects purely from raw image data.