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| 다중 양식 토픽 모델링× | 다중 모달 트랜스포머× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2003–present | 2019–2021 |
| 창시자≠ | Blei, D. M. & Jordan, M. I. (foundational corr-LDA); extended by many authors | Lu et al. (ViLBERT); Radford et al. (CLIP) |
| 유형≠ | Generative probabilistic topic model | Cross-modal attention-based deep learning model |
| 원전≠ | Blei, D. M., & Jordan, M. I. (2003). Modeling annotated data. Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 127–134. DOI ↗ | 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 | Multimodal LDA, multi-modal topic model, cross-modal topic modeling, MM-TM | multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer |
| 관련≠ | 6 | 5 |
| 요약≠ | Multimodal topic modeling discovers latent thematic structure shared across multiple data modalities — for example, co-occurring words and images — by learning a joint probabilistic representation that aligns topics across modalities. It extends classical text-only approaches such as LDA to settings where each document or observation consists of heterogeneous data types. | 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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