Сравнение методов

	Мультимодальная семантическая сегментация	Сегментация экземпляров
Область	Глубокое обучение	Глубокое обучение
Семейство	Machine learning	Machine learning
Год появления	2014–2016	2017
Автор метода	Multiple contributors (Hazirbas et al., Long et al., and others)	He, K., Gkioxari, G., Dollar, P., Girshick, R.
Тип	Pixel-level classification with multi-sensor fusion	Pixel-level detection and mask prediction
Основополагающий источник	Hazirbas, C., Ma, L., Domokos, C., & Cremers, D. (2016). FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture. In Proceedings of the Asian Conference on Computer Vision (ACCV). Springer.	He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2961–2969.
Другие названия	multimodal scene parsing, multi-sensor semantic segmentation, RGB-D semantic segmentation, cross-modal semantic segmentation	instance-level segmentation, object instance segmentation, mask prediction, panoptic instance segmentation
Связанные	3	4
Сводка	Multimodal semantic segmentation assigns a semantic class label to every pixel in a scene by fusing information from two or more sensor modalities — most commonly RGB images paired with depth maps (RGB-D), LiDAR point clouds, thermal cameras, or text descriptions. Deep encoder-decoder networks learn to align and fuse complementary cues from each modality, producing denser and more accurate segmentation than any single-modality approach.	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.