مقایسهٔ روش‌ها

	طبقه‌بندی چندوجهی مبتنی بر BERT	CLIP	ترنسفورمر بینایی
حوزه	یادگیری عمیق	یادگیری عمیق	یادگیری عمیق
خانواده	Machine learning	Machine learning	Machine learning
سال پیدایش	2019	2021	2021
پدیدآور	Kiela, D. et al.; Lu, J. et al.	Radford, A.; Kim, J. W.; et al. (OpenAI)	Dosovitskiy, A. et al.
نوع	Multimodal transformer classifier	Contrastive vision-language pretraining model	Transformer architecture for images (self-attention over patches)
منبع بنیادین	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.	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
نام‌های دیگر	MMBT, multimodal transformer classification, BERT multimodal fusion, vision-language BERT classifier	CLIP, Contrastive Language-Image Pre-training, zero-shot image classifier, visual-language model	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
مرتبط	2	2	5
خلاصه	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.	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).