Uporedite metode

	Multimodalna rekurentna neuronska mreža	Multimodalni Transformer
Oblast	Duboko učenje	Duboko učenje
Porodica	Machine learning	Machine learning
Godina nastanka	2011–2015	2019–2021
Tvorac	Multiple contributors; prominently Ngiam et al. (2011) and Vinyals et al. (2015)	Lu et al. (ViLBERT); Radford et al. (CLIP)
Tip	Multimodal sequence model (recurrent)	Cross-modal attention-based deep learning model
Temeljni izvor	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.
Drugi nazivi	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
Srodne	6	5
Sažetak	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.