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| 다중 양식 강화학습 (Multimodal Reinforcement Learning)× | 멀티모달 비전 트랜스포머× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2015–2022 | 2021 |
| 창시자≠ | Multiple contributors (DeepMind, OpenAI, Google Brain, 2010s–2020s) | Dosovitskiy et al. (ViT); Radford et al. (CLIP multimodal ViT) |
| 유형≠ | Multimodal deep RL agent | Multimodal transformer model |
| 원전≠ | Reed, S., Zolna, K., Parisotto, E., Colmenarejo, S. G., Novikov, A., Barth-Maron, G., ... & de Freitas, N. (2022). A Generalist Agent. Transactions on Machine Learning Research. link ↗ | Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In International Conference on Learning Representations (ICLR). link ↗ |
| 별칭 | Multimodal RL, Multi-Sensory Reinforcement Learning, Vision-Language RL, Multi-Input RL | Multimodal ViT, vision-language transformer, cross-modal vision transformer, multi-modal ViT |
| 관련≠ | 6 | 5 |
| 요약≠ | Multimodal Reinforcement Learning trains agents to make sequential decisions by perceiving and integrating multiple input modalities — such as raw pixels, language instructions, audio, and proprioceptive sensors — simultaneously. Rather than acting on a single data stream, the agent fuses heterogeneous signals into a unified state representation and learns a policy through environmental reward feedback. | Multimodal Vision Transformer (Multimodal ViT) extends the Vision Transformer architecture to jointly process and align representations from multiple modalities — typically images and text — using self-attention and cross-attention mechanisms. By learning shared or aligned embedding spaces across modalities, it enables tasks such as visual question answering, image-text retrieval, visual grounding, and image captioning. |
| ScholarGate데이터셋 ↗ |
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