Compara mètodes

	Resum de text multimodal	Classificació multimodal basada en BERT
Camp	Aprenentatge profund	Aprenentatge profund
Família	Machine learning	Machine learning
Any d'origen	2018	2019
Autor original	Zhu et al. (pioneering MSMO framework)	Kiela, D. et al.; Lu, J. et al.
Tipus	Generative / extractive NLP with visual input	Multimodal transformer classifier
Font seminal	Zhu, J., Li, H., Liu, T., Zhou, Y., Zhang, J., & Zong, C. (2018). MSMO: Multimodal Summarization with Multimodal Output. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), 4154–4164.	Kiela, D., Bhooshan, S., Firooz, H., Perez, E., & Testuggine, D. (2019). Supervised multimodal bitransformers for classifying images and text. arXiv preprint arXiv:1909.02950.
Àlies	MMS, multimodal summarization, cross-modal summarization, vision-language summarization	MMBT, multimodal transformer classification, BERT multimodal fusion, vision-language BERT classifier
Relacionats	5	2
Resum	Multimodal text summarization generates a concise textual summary by jointly processing multiple input modalities — most commonly text and images, but also video frames or audio — using deep learning models that align visual and linguistic representations. The output is a natural-language summary that captures salient content from all available modalities.	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.