So sánh phương pháp

	Word2Vec Đa phương thức	Transformer Đa phương thức
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2014	2019–2021
Người khởi xướng	Bruni, E., Tran, N.-K., & Baroni, M. (building on Mikolov et al.)	Lu et al. (ViLBERT); Radford et al. (CLIP)
Loại	Multimodal word embedding model	Cross-modal attention-based deep learning model
Công trình gốc	Bruni, E., Tran, N.-K., & Baroni, M. (2014). Multimodal Distributional Semantics. Journal of Artificial Intelligence Research, 49, 1–47.	Lu, J., Batra, D., Parikh, D., & Lee, S. (2019). ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks. Advances in Neural Information Processing Systems (NeurIPS), 32.
Tên gọi khác	multimodal word embeddings, visual-linguistic Word2Vec, cross-modal Word2Vec, MM-W2V	multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer
Liên quan	5	5
Tóm tắt	Multimodal Word2Vec extends the classic Word2Vec framework by grounding word representations in perceptual signals — typically image features — alongside distributional text statistics. The result is word vectors that capture both linguistic co-occurrence patterns and visual meaning, enabling richer semantic similarity judgements and better performance on concept-level tasks where purely text-based embeddings fall short.	A Multimodal Transformer extends the standard Transformer architecture to process and jointly reason over two or more input modalities — most commonly text and images, but also audio, video, or structured data. Cross-modal attention layers allow information from one modality to inform representations in another, enabling tasks such as visual question answering, image captioning, and multimodal sentiment analysis.