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	Klasyfikacja multimodalna obrazów	Klasyfikacja obrazów
Dziedzina	Uczenie głębokie	Uczenie głębokie
Rodzina	Machine learning	Machine learning
Rok powstania	2011–2021	2012 (deep CNN era); conceptual roots 1989 (LeCun)
Twórca	Ngiam et al.; Radford et al. (CLIP)	Krizhevsky, A.; Sutskever, I.; Hinton, G. E.
Typ	Multimodal supervised classification	Supervised classification task
Źródło pierwotne	Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., ... & Sutskever, I. (2021). Learning transferable visual models from natural language supervision. Proceedings of the 38th International Conference on Machine Learning (ICML), PMLR 139, 8748–8763.	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.
Inne nazwy	multimodal visual classification, image-text classification, vision-language classification, cross-modal image classification	visual classification, image recognition, CNN-based classification, visual categorization
Pokrewne	6	5
Podsumowanie	Multimodal image classification extends standard visual classification by incorporating additional modalities — such as text captions, audio, or structured metadata — alongside image features. Separate encoders process each modality, their representations are fused, and a joint classifier assigns the target label. Models such as CLIP demonstrate that image–text alignment enables zero-shot and few-shot image classification at scale.	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.