So sánh phương pháp

	Phân loại dựa trên BERT đa phương thức	CLIP
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2019	2021
Người khởi xướng	Kiela, D. et al.; Lu, J. et al.	Radford, A.; Kim, J. W.; et al. (OpenAI)
Loại	Multimodal transformer classifier	Contrastive vision-language pretraining model
Công trình gốc	Kiela, D., Bhooshan, S., Firooz, H., Perez, E., & Testuggine, D. (2019). Supervised multimodal bitransformers for classifying images and text. arXiv preprint arXiv:1909.02950.	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, PMLR 139, 8748–8763.
Tên gọi khác	MMBT, multimodal transformer classification, BERT multimodal fusion, vision-language BERT classifier	CLIP, Contrastive Language-Image Pre-training, zero-shot image classifier, visual-language model
Liên quan	2	2
Tóm tắt	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.	CLIP (Contrastive Language-Image Pretraining) is a vision-language model introduced by Radford et al. at OpenAI in 2021 that jointly learns aligned image and text representations by training on 400 million internet-sourced image-text pairs using a contrastive objective, enabling zero-shot transfer to image classification tasks without any task-specific fine-tuning.