Adapting CLIP to YouTube Data (with Python\u00a0Code)<\/h4>\n
This is the 4th article in a larger series on multimodal AI<\/a>. In the previous post, we discussed multimodal RAG<\/a> systems, which can retrieve and synthesize information from different data modalities (e.g. text, images, audio). There, we saw how we could implement such a system using CLIP. One issue with this approach, however, is that vector search results from a general-purpose embedding model (like CLIP) may perform poorly in domain-specific use cases<\/strong>. In this article, I\u2019ll discuss how we can mitigate these issues via fine-tuning multimodal embedding models.<\/p>\n