Linganisha mbinu

	GRU ya Njia Nyingi (Multimodal GRU)	Multimodal LSTM
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2014–2017	2016
Mwanzilishi	Cho, K. et al. (GRU); adapted to multimodal settings by multiple research groups	Rajagopalan et al. and various concurrent works (2016–2018)
Aina	Recurrent neural network (multimodal variant)	Recurrent neural network architecture
Chanzo asilia	Cho, K., van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. Proceedings of EMNLP 2014, 1724–1734.	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.
Majina mbadala	MM-GRU, Multimodal Gated Recurrent Unit, Cross-modal GRU, Multi-input GRU	MM-LSTM, multimodal recurrent network, multi-input LSTM, multimodal sequence model
Zinazohusiana	6	4
Muhtasari	Multimodal GRU extends the Gated Recurrent Unit architecture to jointly process sequential data from multiple input modalities — such as text, audio, and video frames — within a single recurrent framework. By fusing modality-specific encodings at the input or hidden-state level, it captures temporal dependencies across heterogeneous data streams and is widely used in multimodal sentiment analysis, video understanding, and audio-visual speech recognition.	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.