Salīdzināt metodes

	Daudzmodālu Transformers	Vision Transformer
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2019–2021	2021
Autors	Lu et al. (ViLBERT); Radford et al. (CLIP)	Dosovitskiy, A. et al.
Tips	Cross-modal attention-based deep learning model	Transformer architecture for images (self-attention over patches)
Pirmavots	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.	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
Citi nosaukumi	multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
Saistītās	5	5
Kopsavilkums	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.	The Vision Transformer (ViT), introduced by Dosovitskiy and colleagues in 2021, splits an image into fixed-size patches, treats those patches as a sequence, and applies the Transformer self-attention mechanism to image classification. Given enough training data, it surpasses convolutional neural networks (CNNs).