विधियों की तुलना करें

	मल्टीमॉडल रिकरेंट न्यूरल नेटवर्क	मल्टीमॉडल ट्रांसफार्मर
क्षेत्र	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2011–2015	2019–2021
प्रवर्तक	Multiple contributors; prominently Ngiam et al. (2011) and Vinyals et al. (2015)	Lu et al. (ViLBERT); Radford et al. (CLIP)
प्रकार	Multimodal sequence model (recurrent)	Cross-modal attention-based deep learning model
मौलिक स्रोत	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.
उपनाम	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
संबंधित	6	5
सारांश	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.