Comparer des méthodes

	Segmentation sémantique multimodale	Segmentation d'instances	Segmentation sémantique
Domaine	Apprentissage profond	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning	Machine learning
Année d'origine	2014–2016	2017	2015
Auteur d'origine	Multiple contributors (Hazirbas et al., Long et al., and others)	He, K., Gkioxari, G., Dollar, P., Girshick, R.	Long, J., Shelhamer, E., & Darrell, T.
Type	Pixel-level classification with multi-sensor fusion	Pixel-level detection and mask prediction	Dense prediction / pixel-wise classification
Source fondatrice	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.	Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440.
Alias	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	pixel-wise classification, scene parsing, dense labeling, semantic scene segmentation
Apparentées	3	4	5
Résumé	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.	Semantic segmentation assigns a class label to every pixel in an image, producing a dense, category-annotated map of the scene. Unlike object detection, which draws bounding boxes, it delineates the exact spatial extent of each class, making it indispensable in medical imaging, autonomous driving, satellite analysis, and any task where precise region boundaries matter.