Comparar métodos

	Clasificación de Imágenes Multimodales	Clasificación multimodal basada en BERT
Campo	Aprendizaje profundo	Aprendizaje profundo
Familia	Machine learning	Machine learning
Año de origen	2011–2021	2019
Autor original	Ngiam et al.; Radford et al. (CLIP)	Kiela, D. et al.; Lu, J. et al.
Tipo	Multimodal supervised classification	Multimodal transformer classifier
Fuente seminal	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.	Kiela, D., Bhooshan, S., Firooz, H., Perez, E., & Testuggine, D. (2019). Supervised multimodal bitransformers for classifying images and text. arXiv preprint arXiv:1909.02950.
Alias	multimodal visual classification, image-text classification, vision-language classification, cross-modal image classification	MMBT, multimodal transformer classification, BERT multimodal fusion, vision-language BERT classifier
Relacionados	6	2
Resumen	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.	Multimodal BERT-based classification extends the BERT transformer architecture to jointly encode and classify data from multiple modalities — most commonly text paired with images — by fusing their representations before a final classification head. Introduced prominently around 2019 through models such as MMBT and ViLBERT, it has become a standard approach for tasks where neither text nor image alone carries sufficient information for accurate labeling.