Bandingkan kaedah

	Multimodal Multilayer Perceptron	Penyematan Zarah Pelbagai Mod (Multimodal Sentence Embeddings)
Bidang	Pembelajaran Mendalam	Pembelajaran Mendalam
Keluarga	Machine learning	Machine learning
Tahun asal	2011 (multimodal extension); 1986 (MLP backpropagation)	2013–2021
Pengasas	Ngiam et al. / Rumelhart, Hinton & Williams (MLP foundations)	Frome et al. (DeViSE, 2013); popularized by Radford et al. (CLIP, 2021)
Jenis	Feedforward neural network with multi-stream fusion	Representation learning model
Sumber perintis	Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., & Ng, A. Y. (2011). Multimodal deep learning. In Proceedings of the 28th International Conference on Machine Learning (ICML 2011), pp. 689–696.	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.
Alias	MM-MLP, multimodal MLP, multi-input feedforward network, fusion multilayer perceptron	multimodal embeddings, cross-modal sentence embeddings, vision-language embeddings, joint image-text embeddings
Berkaitan	5	1
Ringkasan	A Multimodal Multilayer Perceptron (MM-MLP) is a feedforward neural network that ingests features from two or more heterogeneous input modalities — such as structured tabular data, text embeddings, and image feature vectors — by encoding each stream separately and fusing them into a shared representation before passing it through fully connected layers to produce a classification or regression output.	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.