Before it
Vision-language models were trained end to end for specific tasks like captioning.
The problem
Retraining a language model for vision throws away its language ability and costs a fortune.
What it introduced
Freeze a pretrained vision encoder and a pretrained language model, and train only cross-attention layers to bridge them.
Why it matters
The bridging recipe that made multimodality affordable, and the template LLaVA and others followed.
What modern models inherit
Bolting vision onto a language model by training an adapter between frozen parts.
What came next
LLaVA did it cheaply and openly; GPT-4V and Gemini made it a product.