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| Monimodaalinen muuntaja× | Vision Transformer× | |
|---|---|---|
| Tieteenala | Syväoppiminen | Syväoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2019–2021 | 2021 |
| Kehittäjä≠ | Lu et al. (ViLBERT); Radford et al. (CLIP) | Dosovitskiy, A. et al. |
| Tyyppi≠ | Cross-modal attention-based deep learning model | Transformer architecture for images (self-attention over patches) |
| Alkuperäislähde≠ | 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. link ↗ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ |
| Rinnakkaisnimet | multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| Liittyvät | 5 | 5 |
| Tiivistelmä≠ | 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). |
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