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| Multimodal Word2Vec× | Monimuotoinen BERT-pohjainen luokittelu× | |
|---|---|---|
| Tieteenala | Syväoppiminen | Syväoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2014 | 2019 |
| Kehittäjä≠ | Bruni, E., Tran, N.-K., & Baroni, M. (building on Mikolov et al.) | Kiela, D. et al.; Lu, J. et al. |
| Tyyppi≠ | Multimodal word embedding model | Multimodal transformer classifier |
| Alkuperäislähde≠ | Bruni, E., Tran, N.-K., & Baroni, M. (2014). Multimodal Distributional Semantics. Journal of Artificial Intelligence Research, 49, 1–47. DOI ↗ | 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 ↗ |
| Rinnakkaisnimet | multimodal word embeddings, visual-linguistic Word2Vec, cross-modal Word2Vec, MM-W2V | MMBT, multimodal transformer classification, BERT multimodal fusion, vision-language BERT classifier |
| Liittyvät≠ | 5 | 2 |
| Tiivistelmä≠ | Multimodal Word2Vec extends the classic Word2Vec framework by grounding word representations in perceptual signals — typically image features — alongside distributional text statistics. The result is word vectors that capture both linguistic co-occurrence patterns and visual meaning, enabling richer semantic similarity judgements and better performance on concept-level tasks where purely text-based embeddings fall short. | 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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