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| Multimodaler Convolutional Neural Network× | Bildklassifizierung× | |
|---|---|---|
| Fachgebiet | Deep Learning | Deep Learning |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2011 | 2012 (deep CNN era); conceptual roots 1989 (LeCun) |
| Urheber≠ | Ngiam, J. et al. / multiple groups | Krizhevsky, A.; Sutskever, I.; Hinton, G. E. |
| Typ≠ | Multimodal deep learning model | Supervised classification task |
| Wegweisende Quelle≠ | Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., & Ng, A. Y. (2011). Multimodal deep learning. In Proceedings of the 28th International Conference on Machine Learning (ICML), 689–696. link ↗ | Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems (NeurIPS), 25, 1097–1105. link ↗ |
| Aliasnamen | MM-CNN, multimodal CNN, multi-input CNN, cross-modal convolutional network | visual classification, image recognition, CNN-based classification, visual categorization |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | A Multimodal Convolutional Neural Network (MM-CNN) processes and fuses two or more input modalities — such as images and text, or video and audio — through dedicated convolutional branches, learning a shared representation that captures complementary signals from each source. The fused representation drives a downstream task such as classification, regression, or retrieval. | Image classification is the task of assigning a single semantic label to an entire image from a fixed set of categories. Modern approaches rely on deep convolutional neural networks (CNNs) or Vision Transformers (ViTs) trained end-to-end on large labeled datasets such as ImageNet, achieving superhuman accuracy on many benchmarks and underpinning applications from medical imaging to autonomous vehicles. |
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