手法を比較

	Multimodal Recurrent Neural Network	リカレントニューラルネットワーク (RNN)
分野	深層学習	深層学習
系統	Machine learning	Machine learning
提唱年	2011–2015	1986–1990
提唱者	Multiple contributors; prominently Ngiam et al. (2011) and Vinyals et al. (2015)	Rumelhart, D. E.; Elman, J. L.
種類	Multimodal sequence model (recurrent)	Sequential neural network
原典	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.	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
別名	MM-RNN, multimodal sequence model, cross-modal RNN, multimodal recurrent encoder-decoder	RNN, Elman network, Jordan network, simple recurrent network
関連	6	3
概要	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.	A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models.