Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Daudzmodālu konvolūcijas neironu tīkls× | Multimodāla BERT klasifikācija× | |
|---|---|---|
| Nozare | Dziļā mācīšanās | Dziļā mācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2011 | 2019 |
| Autors≠ | Ngiam, J. et al. / multiple groups | Kiela, D. et al.; Lu, J. et al. |
| Tips≠ | Multimodal deep learning model | Multimodal transformer classifier |
| Pirmavots≠ | Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., & Ng, A. Y. (2011). Multimodal deep learning. In Proceedings of the 28th International Conference on Machine Learning (ICML), 689–696. link ↗ | 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 ↗ |
| Citi nosaukumi | MM-CNN, multimodal CNN, multi-input CNN, cross-modal convolutional network | MMBT, multimodal transformer classification, BERT multimodal fusion, vision-language BERT classifier |
| Saistītās≠ | 5 | 2 |
| Kopsavilkums≠ | A Multimodal Convolutional Neural Network (MM-CNN) processes and fuses two or more input modalities — such as images and text, or video and audio — through dedicated convolutional branches, learning a shared representation that captures complementary signals from each source. The fused representation drives a downstream task such as classification, regression, or retrieval. | 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. |
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