So sánh phương pháp

	Multimodal GRU	Transformer Đa phương thức
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2014–2017	2019–2021
Người khởi xướng	Cho, K. et al. (GRU); adapted to multimodal settings by multiple research groups	Lu et al. (ViLBERT); Radford et al. (CLIP)
Loại	Recurrent neural network (multimodal variant)	Cross-modal attention-based deep learning model
Công trình gốc	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.	Lu, J., Batra, D., Parikh, D., & Lee, S. (2019). ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks. Advances in Neural Information Processing Systems (NeurIPS), 32.
Tên gọi khác	MM-GRU, Multimodal Gated Recurrent Unit, Cross-modal GRU, Multi-input GRU	multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer
Liên quan	6	5
Tóm tắt	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.	A Multimodal Transformer extends the standard Transformer architecture to process and jointly reason over two or more input modalities — most commonly text and images, but also audio, video, or structured data. Cross-modal attention layers allow information from one modality to inform representations in another, enabling tasks such as visual question answering, image captioning, and multimodal sentiment analysis.