방법 비교

	Multimodal Recurrent Neural Network	다중 양식 합성곱 신경망
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2011–2015	2011
창시자	Multiple contributors; prominently Ngiam et al. (2011) and Vinyals et al. (2015)	Ngiam, J. et al. / multiple groups
유형	Multimodal sequence model (recurrent)	Multimodal deep learning model
원전	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.	Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., & Ng, A. Y. (2011). Multimodal deep learning. In Proceedings of the 28th International Conference on Machine Learning (ICML), 689–696.
별칭	MM-RNN, multimodal sequence model, cross-modal RNN, multimodal recurrent encoder-decoder	MM-CNN, multimodal CNN, multi-input CNN, cross-modal convolutional network
관련	6	5
요약	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 Multimodal Convolutional Neural Network (MM-CNN) processes and fuses two or more input modalities — such as images and text, or video and audio — through dedicated convolutional branches, learning a shared representation that captures complementary signals from each source. The fused representation drives a downstream task such as classification, regression, or retrieval.