Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Мультимодальная LSTM× | Механизм внимания× | |
|---|---|---|
| Область | Глубокое обучение | Глубокое обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2016 | 2015 |
| Автор метода≠ | Rajagopalan et al. and various concurrent works (2016–2018) | Bahdanau, D.; Luong, M.T. |
| Тип≠ | Recurrent neural network architecture | Neural attention layer (encoder-decoder) |
| Основополагающий источник≠ | Rajagopalan, S., Tran, L., Rozgic, V., Narayanan, S., Kumar, A., & Ramakrishna, S. (2016). Extending Long Short-Term Memory for Multi-View Structured Learning. In Proceedings of ECCV 2016. Springer. link ↗ | Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. ICLR. link ↗ |
| Другие названия≠ | MM-LSTM, multimodal recurrent network, multi-input LSTM, multimodal sequence model | Dikkat Mekanizması (Bahdanau / Luong Attention), dikkat mekanizmasi, neural attention, additive attention |
| Связанные≠ | 4 | 5 |
| Сводка≠ | Multimodal LSTM extends the standard Long Short-Term Memory network to jointly process sequential data from multiple input modalities — such as text, audio, and video — within a unified recurrent architecture. By fusing representations from different sources before or within the LSTM cells, it captures temporal dependencies that span and cross modalities, making it a foundational approach for tasks like sentiment analysis, video captioning, and affective computing. | The attention mechanism, introduced by Bahdanau, Cho and Bengio in 2015 and refined by Luong, Pham and Manning the same year, lets a sequence decoder dynamically learn which of the encoder's outputs to focus on at each step. Before the Transformer, it substantially improved machine-translation quality by freeing models from compressing an entire input into a single fixed vector. |
| ScholarGateНабор данных ↗ |
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