What are the main challenges of multimodal learning?

What are the main challenges of multimodal learning?

Multimodal learning involves integrating information from different modalities, like text, images, and audio. Some challenges include data alignment, model complexity, and the need for large datasets.

Can you explain data alignment in more detail?

Data alignment refers to the process of ensuring that data from different modalities corresponds correctly. For example, aligning text descriptions with relevant images is crucial for effective learning.

What about the model complexity? Why is it a challenge?

Model complexity arises because multimodal models need to process and integrate diverse data types, which can lead to increased computational requirements and difficulties in training.

Are there any solutions to these challenges?

Yes, researchers are exploring techniques like attention mechanisms, transfer learning, and better data augmentation strategies to address these challenges.