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	Multimodal GRU	Klasyfikacja multimodalna oparta na BERT
Dziedzina	Uczenie głębokie	Uczenie głębokie
Rodzina	Machine learning	Machine learning
Rok powstania	2014–2017	2019
Twórca	Cho, K. et al. (GRU); adapted to multimodal settings by multiple research groups	Kiela, D. et al.; Lu, J. et al.
Typ	Recurrent neural network (multimodal variant)	Multimodal transformer classifier
Źródło pierwotne	Cho, K., van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. Proceedings of EMNLP 2014, 1724–1734.	Kiela, D., Bhooshan, S., Firooz, H., Perez, E., & Testuggine, D. (2019). Supervised multimodal bitransformers for classifying images and text. arXiv preprint arXiv:1909.02950.
Inne nazwy	MM-GRU, Multimodal Gated Recurrent Unit, Cross-modal GRU, Multi-input GRU	MMBT, multimodal transformer classification, BERT multimodal fusion, vision-language BERT classifier
Pokrewne	6	2
Podsumowanie	Multimodal GRU extends the Gated Recurrent Unit architecture to jointly process sequential data from multiple input modalities — such as text, audio, and video frames — within a single recurrent framework. By fusing modality-specific encodings at the input or hidden-state level, it captures temporal dependencies across heterogeneous data streams and is widely used in multimodal sentiment analysis, video understanding, and audio-visual speech recognition.	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.