Порівняння методів

	Багатомодальна обробка природної мови	BERT Embeddings
Галузь	Інтелектуальний аналіз тексту	Інтелектуальний аналіз тексту
Родина	Process / pipeline	Process / pipeline
Рік появи	2021 (modern era, CLIP onward)	2019
Автор методу	Radford et al. (OpenAI) — CLIP, 2021; Li et al. — BLIP-2, 2023	Devlin, Chang, Lee & Toutanova (Google AI)
Тип	Cross-modal understanding and generation pipeline	Contextual transformer text-representation method
Основоположне джерело	Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021). Learning Transferable Visual Models From Natural Language Supervision. Proceedings of the 38th International Conference on Machine Learning (ICML), 8748–8763.	Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186.
Інші назви	Çok Kipli NLP (Multimodal NLP), vision-language models, multimodal learning	contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri
Пов'язані	4	4
Підсумок	Multimodal NLP is a family of natural-language-processing pipelines that combine text with one or more additional data modalities — most commonly images, but also audio and video — to perform understanding and generation tasks such as visual question answering, image captioning, and multimodal sentiment recognition. The field gained its modern form with CLIP (Radford et al., 2021) and has since advanced through architectures such as BLIP-2 (Li et al., 2023) that bridge frozen image encoders and large language models.	BERT-based text embeddings, introduced by Devlin and colleagues at Google AI in 2019, turn text into context-sensitive dense vectors using a bidirectional Transformer encoder. Because the meaning of a word shifts with its context, BERT produces richer representations than static methods such as Word2Vec or topic models like LDA.