So sánh phương pháp

	Phân loại ảnh đa phương thức	Phân loại ảnh
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2011–2021	2012 (deep CNN era); conceptual roots 1989 (LeCun)
Người khởi xướng	Ngiam et al.; Radford et al. (CLIP)	Krizhevsky, A.; Sutskever, I.; Hinton, G. E.
Loại	Multimodal supervised classification	Supervised classification task
Công trình gốc	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.
Tên gọi khác	multimodal visual classification, image-text classification, vision-language classification, cross-modal image classification	visual classification, image recognition, CNN-based classification, visual categorization
Liên quan	6	5
Tóm tắt	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.