방법 비교

	다중 모드 GRU	다중 모달 트랜스포머
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2014–2017	2019–2021
창시자	Cho, K. et al. (GRU); adapted to multimodal settings by multiple research groups	Lu et al. (ViLBERT); Radford et al. (CLIP)
유형	Recurrent neural network (multimodal variant)	Cross-modal attention-based deep learning model
원전	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.	Lu, J., Batra, D., Parikh, D., & Lee, S. (2019). ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks. Advances in Neural Information Processing Systems (NeurIPS), 32.
별칭	MM-GRU, Multimodal Gated Recurrent Unit, Cross-modal GRU, Multi-input GRU	multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer
관련	6	5
요약	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.	A Multimodal Transformer extends the standard Transformer architecture to process and jointly reason over two or more input modalities — most commonly text and images, but also audio, video, or structured data. Cross-modal attention layers allow information from one modality to inform representations in another, enabling tasks such as visual question answering, image captioning, and multimodal sentiment analysis.