Compară metode

	Embeddings multimodale de propoziții	CLIP
Domeniu	Învățare profundă	Învățare profundă
Familie	Machine learning	Machine learning
Anul apariției	2013–2021	2021
Autorul original	Frome et al. (DeViSE, 2013); popularized by Radford et al. (CLIP, 2021)	Radford, A.; Kim, J. W.; et al. (OpenAI)
Tip	Representation learning model	Contrastive vision-language pretraining model
Sursa seminală	Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., ... & Sutskever, I. (2021). Learning transferable visual models from natural language supervision. In Proceedings of the 38th International Conference on Machine Learning (ICML), pp. 8748–8763. PMLR.	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.
Denumiri alternative	multimodal embeddings, cross-modal sentence embeddings, vision-language embeddings, joint image-text embeddings	CLIP, Contrastive Language-Image Pre-training, zero-shot image classifier, visual-language model
Înrudite	1	2
Rezumat	Multimodal sentence embeddings map text and images (and sometimes audio or video) into a shared continuous vector space, so that semantically related pairs from different modalities land close together. Trained by contrastive objectives on large paired corpora, these representations power cross-modal retrieval, zero-shot classification, and vision-language reasoning.	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.