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| Multimodal Recurrent Neural Network× | 순환 신경망× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2011–2015 | 1986–1990 |
| 창시자≠ | Multiple contributors; prominently Ngiam et al. (2011) and Vinyals et al. (2015) | Rumelhart, D. E.; Elman, J. L. |
| 유형≠ | Multimodal sequence model (recurrent) | Sequential neural network |
| 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. DOI ↗ | Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. DOI ↗ |
| 별칭 | MM-RNN, multimodal sequence model, cross-modal RNN, multimodal recurrent encoder-decoder | RNN, Elman network, Jordan network, simple recurrent network |
| 관련≠ | 6 | 3 |
| 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 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. |
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