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| Multimodal Multilayer Perceptron× | 다중 모달 트랜스포머× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2011 (multimodal extension); 1986 (MLP backpropagation) | 2019–2021 |
| 창시자≠ | Ngiam et al. / Rumelhart, Hinton & Williams (MLP foundations) | Lu et al. (ViLBERT); Radford et al. (CLIP) |
| 유형≠ | Feedforward neural network with multi-stream fusion | Cross-modal attention-based deep learning model |
| 원전≠ | 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. link ↗ | 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. link ↗ |
| 별칭 | MM-MLP, multimodal MLP, multi-input feedforward network, fusion multilayer perceptron | multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer |
| 관련 | 5 | 5 |
| 요약≠ | 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. | 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. |
| ScholarGate데이터셋 ↗ |
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