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Related Questions
- What are the key differences between text-only and multimodal pre-training, and how do they impact model performance?
- Can you explain the concept of multimodal fine-tuning and its role in adapting pre-trained models to specific tasks?
- How can I select the most suitable pre-training objectives and architectures for my multimodal task, considering factors like data availability and task complexity?
- What are some common challenges in multimodal fine-tuning, such as handling domain shift or imbalanced data, and how can I address them?
- How can I leverage transfer learning from pre-trained multimodal models to improve performance on related tasks, and what are some best practices for doing so?
- Can you provide guidance on how to design and implement effective multimodal training objectives, such as contrastive learning or masked language modeling, for my specific task?
- What are some recent advances and research directions in multimodal pre-training and fine-tuning, and how can I apply them to my project?
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