Machine learningDeep learning / NLP / CV
多模态语义分割
多模态语义分割通过融合来自两个或多个传感器模态的信息，为场景中的每个像素分配语义类别标签——最常见的是将RGB图像与深度图（RGB-D）、激光雷达点云、热成像相机或文本描述配对。深度编码器-解码器网络学习对齐和融合来自每个模态的互补线索，从而产生比任何单一模态方法更密集、更准确的分割结果。
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来源
- 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. link ↗
- Zhang, J., Liu, H., Yang, K., Hu, X., Liu, R., & Stiefelhagen, R. (2023). CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers. IEEE Transactions on Intelligent Transportation Systems, 24(12), 14801–14813. DOI: 10.1109/TITS.2023.3300537 ↗
如何引用本页
ScholarGate. (2026, June 3). Multimodal Semantic Segmentation (Multi-Sensor Pixel-Level Scene Understanding). ScholarGate. https://scholargate.app/zh/deep-learning/multimodal-semantic-segmentation
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