เปรียบเทียบวิธี

	การประมวลผลภาษาธรรมชาติแบบหลายรูปแบบ	BERT Embeddings	วิชันทรานส์ฟอร์มเมอร์
สาขาวิชา	การทำเหมืองข้อความ	การทำเหมืองข้อความ	การเรียนรู้เชิงลึก
ตระกูล	Process / pipeline	Process / pipeline	Machine learning
ปีกำเนิด	2021 (modern era, CLIP onward)	2019	2021
ผู้ริเริ่ม	Radford et al. (OpenAI) — CLIP, 2021; Li et al. — BLIP-2, 2023	Devlin, Chang, Lee & Toutanova (Google AI)	Dosovitskiy, A. et al.
ประเภท	Cross-modal understanding and generation pipeline	Contextual transformer text-representation method	Transformer architecture for images (self-attention over patches)
แหล่งต้นตำรับ	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.	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
ชื่อเรียกอื่น	Çok Kipli NLP (Multimodal NLP), vision-language models, multimodal learning	contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
ที่เกี่ยวข้อง	4	4	5
สรุป	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.	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).