Prismer: A Vision-Language Model with Multi-Task Experts
This addresses the scalability issue in vision-language models for researchers and practitioners by offering a more efficient alternative, though it is incremental as it builds on existing expert models.
The paper tackles the problem of training large vision-language models by introducing Prismer, a data- and parameter-efficient model that uses frozen, pre-trained task-specific experts, achieving competitive performance with up to 100 times less training data.
Recent vision-language models have shown impressive multi-modal generation capabilities. However, typically they require training huge models on massive datasets. As a more scalable alternative, we introduce Prismer, a data- and parameter-efficient vision-language model that leverages an ensemble of task-specific experts. Prismer only requires training of a small number of components, with the majority of network weights inherited from multiple readily-available, pre-trained experts, and kept frozen during training. By leveraging experts from a wide range of domains, we show Prismer can efficiently pool this expert knowledge and adapt it to various vision-language reasoning tasks. In our experiments, we show that Prismer achieves fine-tuned and few-shot learning performance which is competitive with current state-of-the-arts, whilst requiring up to two orders of magnitude less training data. Code is available at https://github.com/NVlabs/prismer.