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| Мултимодална рекурентна невронна мрежа× | Дългосрочна краткосрочна памет (LSTM)× | |
|---|---|---|
| Област | Дълбоко обучение | Дълбоко обучение |
| Семейство | Machine learning | Machine learning |
| Година на възникване≠ | 2011–2015 | 1997 |
| Създател≠ | Multiple contributors; prominently Ngiam et al. (2011) and Vinyals et al. (2015) | Hochreiter, S. & Schmidhuber, J. |
| Тип≠ | Multimodal sequence model (recurrent) | Recurrent neural network with gated memory cells |
| Основополагащ източник≠ | 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 ↗ | Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. DOI ↗ |
| Други названия | MM-RNN, multimodal sequence model, cross-modal RNN, multimodal recurrent encoder-decoder | LSTM, LSTM network, LSTM-RNN, long short-term memory RNN |
| Свързани≠ | 6 | 4 |
| Резюме≠ | 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. | Long Short-Term Memory (LSTM) is a gated recurrent neural network architecture introduced by Hochreiter and Schmidhuber in 1997. It was designed to learn dependencies across long sequences by using dedicated memory cells and three learned gates — forget, input, and output — that control what information is retained, updated, or passed forward at each time step. |
| ScholarGateНабор от данни ↗ |
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