Synthetic Caption

Synthetic captioning focuses on generating artificial image descriptions to augment or replace real-world captions in training multimodal models, primarily aiming to improve model performance and efficiency. Current research emphasizes optimizing synthetic caption generation pipelines, exploring the interplay between synthetic and real captions, and investigating the impact of different caption formats on various model architectures, including CLIP, multimodal LLMs, and diffusion models. This work is significant because high-quality training data is a major bottleneck in multimodal learning, and synthetic captions offer a scalable and potentially cost-effective solution for improving model accuracy and generalizability across diverse vision-language tasks.