방법 비교

	다중 모드 GRU	Multimodal Recurrent Neural Network
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2014–2017	2011–2015
창시자	Cho, K. et al. (GRU); adapted to multimodal settings by multiple research groups	Multiple contributors; prominently Ngiam et al. (2011) and Vinyals et al. (2015)
유형	Recurrent neural network (multimodal variant)	Multimodal sequence model (recurrent)
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.	Vinyals, O., Toshev, A., Bengio, S., & Erhan, D. (2015). Show and Tell: A Neural Image Caption Generator. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3156–3164.
별칭	MM-GRU, Multimodal Gated Recurrent Unit, Cross-modal GRU, Multi-input GRU	MM-RNN, multimodal sequence model, cross-modal RNN, multimodal recurrent encoder-decoder
관련	6	6
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 Recurrent Neural Network combines inputs from two or more data modalities — such as images, text, and audio — within a recurrent sequence-processing framework. It encodes each modality separately, fuses the representations, and then processes the combined signal through recurrent units (RNN, LSTM, or GRU) to generate or classify sequential outputs. This design made it a foundational approach in image captioning, video description, and audio-visual speech recognition.