Synth$^2$: Boosting Visual-Language Models with Synthetic Captions and
  Image Embeddings
Sahand Sharifzadeh Christos Kaplanis Shreya Pathak Dharshan Kumaran Anastasija Ilic Jovana Mitrovic Charles Blundell Andrea Banino
The creation of high-quality human-labeled image-caption datasets presents a
significant bottleneck in the development of Visual-Language Models (VLMs). We
propose a novel approach that leverages the strengths of Large Language Models
(LLMs) and image generation models to create synthetic image-text pairs for
efficient and effective VLM training. Our method employs pretraining a
text-to-image model to synthesize image embeddings starting from captions
generated by an LLM. These synthetic pairs are then used to train a VLM.
Extensive experiments demonstrate that the VLM trained with synthetic data
exhibits comparable performance on image captioning, while requiring a fraction
of the data used by models trained solely on human-annotated data. In
particular, we outperform the baseline by 17% through augmentation with a
synthetic dataset. Furthermore, we show that synthesizing in the image
embedding space is 25% faster than in the pixel space. This research introduces
a promising technique for generating large-scale, customizable image datasets,
leading to enhanced VLM performance and wider applicability across various
domains, all with improved data efficiency and resource utilization.