방법 비교

	Multimodal Recurrent Neural Network	다중 모달 트랜스포머
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2011–2015	2019–2021
창시자	Multiple contributors; prominently Ngiam et al. (2011) and Vinyals et al. (2015)	Lu et al. (ViLBERT); Radford et al. (CLIP)
유형	Multimodal sequence model (recurrent)	Cross-modal attention-based deep learning model
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.	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.
별칭	MM-RNN, multimodal sequence model, cross-modal RNN, multimodal recurrent encoder-decoder	multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer
관련	6	5
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 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.