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| Multimodale BERT-basierte Klassifikation× | Vision Transformer× | |
|---|---|---|
| Fachgebiet | Deep Learning | Deep Learning |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2019 | 2021 |
| Urheber≠ | Kiela, D. et al.; Lu, J. et al. | Dosovitskiy, A. et al. |
| Typ≠ | Multimodal transformer classifier | Transformer architecture for images (self-attention over patches) |
| Wegweisende Quelle≠ | Kiela, D., Bhooshan, S., Firooz, H., Perez, E., & Testuggine, D. (2019). Supervised multimodal bitransformers for classifying images and text. arXiv preprint arXiv:1909.02950. link ↗ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ |
| Aliasnamen | MMBT, multimodal transformer classification, BERT multimodal fusion, vision-language BERT classifier | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| Verwandt≠ | 2 | 5 |
| Zusammenfassung≠ | Multimodal BERT-based classification extends the BERT transformer architecture to jointly encode and classify data from multiple modalities — most commonly text paired with images — by fusing their representations before a final classification head. Introduced prominently around 2019 through models such as MMBT and ViLBERT, it has become a standard approach for tasks where neither text nor image alone carries sufficient information for accurate labeling. | 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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