Recaptioning: Upgrading Your Image-Text Data for Better Model Alignment 🚀

Recaptioning: Engineering High-Quality Descriptions for Multi-modal Models 🚀 In multi-modal AI, we often face the "Garbage In, Garbage Out" problem: scraped image captions are often too vague ("a pretty cup"), too long (exceeding the 77-token limit), or simply incorrect. Recaptioning is the process of rewriting or regenerating these descriptions to ensure they are model-ready and semantically dense. Based on the data_engineering_book , this post covers why you need recaptioning, the core strategies to implement it, and how to evaluate the results. 1. Why Recaptioning is a Game Changer Improve Semantic Alignment: Fix vague or fictional descriptions to match 100% of the image content. Adapt to Model Constraints: Shorten long sentences to fit token limits (e.g., CLIP's 77-token bottleneck) without losing core info. Multi-dimensional Coverage: Generate multiple captions covering "Appearance," "Texture," and "Context" to improve retrieval robustness. Standardize Style: Clean up slang, typos