手法を比較

	Multimodal Recurrent Neural Network	Gated Recurrent Unit (GRU)
分野	深層学習	深層学習
系統	Machine learning	Machine learning
提唱年	2011–2015	2014
提唱者	Multiple contributors; prominently Ngiam et al. (2011) and Vinyals et al. (2015)	Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y.
種類	Multimodal sequence model (recurrent)	Recurrent neural network with gating
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.	Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. In Proceedings of EMNLP 2014, pp. 1724–1734.
別名	MM-RNN, multimodal sequence model, cross-modal RNN, multimodal recurrent encoder-decoder	GRU, GRU network, gated RNN, GRU cell
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.	The Gated Recurrent Unit (GRU), introduced by Cho et al. in 2014, is a streamlined recurrent neural network that uses two learned gates — an update gate and a reset gate — to selectively retain or discard information across time steps, enabling effective sequence modelling with fewer parameters than LSTM.