পদ্ধতির তুলনা করুন

	মাল্টিমোডাল BERT-ভিত্তিক ক্লাসিফিকেশন	ভিশন ট্রান্সফরমার
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2019	2021
প্রবর্তক	Kiela, D. et al.; Lu, J. et al.	Dosovitskiy, A. et al.
ধরন	Multimodal transformer classifier	Transformer architecture for images (self-attention over patches)
মৌলিক উৎস	Kiela, D., Bhooshan, S., Firooz, H., Perez, E., & Testuggine, D. (2019). Supervised multimodal bitransformers for classifying images and text. arXiv preprint arXiv:1909.02950.	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
অপর নাম	MMBT, multimodal transformer classification, BERT multimodal fusion, vision-language BERT classifier	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
সম্পর্কিত	2	5
সারসংক্ষেপ	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.	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).